Transforming data in a distributed storage and task network

ABSTRACT

A method begins with a computing device dividing data into data partitions. For a data partition of the data partitions, the method continues with the computing device associating indexing information with the data partition. The method continues with the computing device segmenting the data partition into a plurality of data segments. The method continues with the computing device dispersed storage error encoding the plurality of data segments to produce a plurality of sets of encoded data slices. The method continues with the computing device grouping encoded data slices of the plurality of sets of encoded data slices to produce a set of groupings of encoded data slices.

CROSS REFERENCE TO RELATED PATENTS

The present U.S. Utility Patent Application claims priority pursuant to35 U.S.C. §120 as a continuation of U.S. Utility application Ser. No.13/707,542 (now issued U.S. Pat. No. 9,015,556), entitled “TRANSFORMINGDATA IN A DISTRIBUTED STORAGE AND TASK NETWORK”, filed Dec. 6, 2012,which claims priority pursuant to 35 U.S.C. §119(e) to U.S. ProvisionalApplication No. 61/569,387, entitled “DISTRIBUTED STORAGE AND TASKPROCESSING”, filed Dec. 12, 2011, both of which are hereby incorporatedherein by reference in their entirety and made part of the present U.S.Utility Patent Application for all purposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

NOT APPLICABLE

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

NOT APPLICABLE

BACKGROUND OF THE INVENTION

1. Technical Field of the Invention

This invention relates generally to computer networks and moreparticularly to dispersed storage of data and distributed taskprocessing of data.

2. Description of Related Art

Computing devices are known to communicate data, process data, and/orstore data. Such computing devices range from wireless smart phones,laptops, tablets, personal computers (PC), work stations, and video gamedevices, to data centers that support millions of web searches, stocktrades, or on-line purchases every day. In general, a computing deviceincludes a central processing unit (CPU), a memory system, userinput/output interfaces, peripheral device interfaces, and aninterconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using“cloud computing” to perform one or more computing functions (e.g., aservice, an application, an algorithm, an arithmetic logic function,etc.) on behalf of the computer. Further, for large services,applications, and/or functions, cloud computing may be performed bymultiple cloud computing resources in a distributed manner to improvethe response time for completion of the service, application, and/orfunction. For example, Hadoop is an open source software framework thatsupports distributed applications enabling application execution bythousands of computers.

In addition to cloud computing, a computer may use “cloud storage” aspart of its memory system. As is known, cloud storage enables a user,via its computer, to store files, applications, etc., on an Internetstorage system. The Internet storage system may include a RAID(redundant array of independent disks) system and/or a dispersed storagesystem that uses an error correction scheme to encode data for storage.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a distributedcomputing system in accordance with the present invention;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the present invention;

FIG. 3 is a diagram of an example of a distributed storage and taskprocessing in accordance with the present invention;

FIG. 4 is a schematic block diagram of an embodiment of an outbounddistributed storage and/or task (DST) processing in accordance with thepresent invention;

FIG. 5 is a logic diagram of an example of a method for outbound DSTprocessing in accordance with the present invention;

FIG. 6 is a schematic block diagram of an embodiment of a dispersederror encoding in accordance with the present invention;

FIG. 7 is a diagram of an example of a segment processing of thedispersed error encoding in accordance with the present invention;

FIG. 8 is a diagram of an example of error encoding and slicingprocessing of the dispersed error encoding in accordance with thepresent invention;

FIG. 9 is a diagram of an example of grouping selection processing ofthe outbound DST processing in accordance with the present invention;

FIG. 10 is a diagram of an example of converting data into slice groupsin accordance with the present invention;

FIG. 11 is a schematic block diagram of an embodiment of a DST executionunit in accordance with the present invention;

FIG. 12 is a schematic block diagram of an example of operation of a DSTexecution unit in accordance with the present invention;

FIG. 13 is a schematic block diagram of an embodiment of an inbounddistributed storage and/or task (DST) processing in accordance with thepresent invention;

FIG. 14 is a logic diagram of an example of a method for inbound DSTprocessing in accordance with the present invention;

FIG. 15 is a diagram of an example of de-grouping selection processingof the inbound DST processing in accordance with the present invention;

FIG. 16 is a schematic block diagram of an embodiment of a dispersederror decoding in accordance with the present invention;

FIG. 17 is a diagram of an example of de-slicing and error decodingprocessing of the dispersed error decoding in accordance with thepresent invention;

FIG. 18 is a diagram of an example of a de-segment processing of thedispersed error decoding in accordance with the present invention;

FIG. 19 is a diagram of an example of converting slice groups into datain accordance with the present invention;

FIG. 20 is a diagram of an example of a distributed storage within thedistributed computing system in accordance with the present invention;

FIG. 21 is a schematic block diagram of an example of operation ofoutbound distributed storage and/or task (DST) processing for storingdata in accordance with the present invention;

FIG. 22 is a schematic block diagram of an example of a dispersed errorencoding for the example of FIG. 21 in accordance with the presentinvention;

FIG. 23 is a diagram of an example of converting data into pillar slicegroups for storage in accordance with the present invention;

FIG. 24 is a schematic block diagram of an example of a storageoperation of a DST execution unit in accordance with the presentinvention;

FIG. 25 is a schematic block diagram of an example of operation ofinbound distributed storage and/or task (DST) processing for retrievingdispersed error encoded data in accordance with the present invention;

FIG. 26 is a schematic block diagram of an example of a dispersed errordecoding for the example of FIG. 25 in accordance with the presentinvention;

FIG. 27 is a schematic block diagram of an example of a distributedstorage and task processing network (DSTN) module storing a plurality ofdata and a plurality of task codes in accordance with the presentinvention;

FIG. 28 is a schematic block diagram of an example of the distributedcomputing system performing tasks on stored data in accordance with thepresent invention;

FIG. 29 is a schematic block diagram of an embodiment of a taskdistribution module facilitating the example of FIG. 28 in accordancewith the present invention;

FIG. 30 is a diagram of a specific example of the distributed computingsystem performing tasks on stored data in accordance with the presentinvention;

FIG. 31 is a schematic block diagram of an example of a distributedstorage and task processing network (DSTN) module storing data and taskcodes for the example of FIG. 30 in accordance with the presentinvention;

FIG. 32 is a diagram of an example of DST allocation information for theexample of FIG. 30 in accordance with the present invention;

FIGS. 33-38 are schematic block diagrams of the DSTN module performingthe example of FIG. 30 in accordance with the present invention;

FIG. 39 is a diagram of an example of combining result information intofinal results for the example of FIG. 30 in accordance with the presentinvention;

FIG. 40A is a diagram of an example embodiment of a dispersed storageand task execution unit in accordance with the present invention;

FIG. 40B is a flowchart illustrating an example of storing andprocessing a group of slices in accordance with the present invention.

FIG. 41 is a flowchart illustrating another example of storing andprocessing a group of slices in accordance with the present invention;

FIG. 42A is a schematic block diagram of another embodiment of adistributed computing system in accordance with the present invention;

FIG. 42B is a flowchart illustrating an example of distributed computingof a task on data in accordance with the present invention;

FIG. 42C is a schematic block diagram of another embodiment of adistributed computing system in accordance with the present invention;

FIG. 42D is a flowchart illustrating an example of distributed computingof a task on stored data in accordance with the present invention;

FIG. 43A is a schematic block diagram of another embodiment of adistributed computing system in accordance with the present invention;

FIG. 43B is a flowchart illustrating an example of performing a partialtask in accordance with the present invention;

FIG. 44A is a diagram of an example embodiment of a dispersed storageand task unit in accordance with the present invention;

FIG. 44B is a schematic block diagram of another embodiment of adistributed computing system in accordance with the present invention;

FIG. 44C is a flowchart illustrating an example of analyzing data inaccordance with the present invention;

FIG. 45 is a flowchart illustrating an example of searching a data indexin accordance with the present invention;

FIG. 46 is a flowchart illustrating another example of searching a dataindex in accordance with the present invention;

FIG. 47A is a flowchart illustrating an example of initiating thresholdcomputing in accordance with the present invention;

FIG. 47B is a flowchart illustrating an example of processing athreshold computing task in accordance with the present invention;

FIG. 48A is a flowchart illustrating an example of generating a task inaccordance with the invention;

FIG. 48B is a flowchart illustrating an example of initiating a task inaccordance with the present invention;

FIG. 49 is a flowchart illustrating another example of ingesting data inaccordance with the present invention;

FIG. 50 is a flowchart illustrating an example of modifying a slicegrouping in accordance with the present invention;

FIG. 51 is a flowchart illustrating an example of further processing ofa group of slices in accordance with the present invention;

FIG. 52 is a flowchart illustrating an example of identifying dataassociations in accordance with the present invention;

FIG. 53A is a diagram illustrating encoding of data in accordance withthe present invention;

FIG. 53B is a flowchart illustrating an example of generating a slicegrouping in accordance with the present invention;

FIG. 54 is a flow chart illustrating an example of selecting distributedcomputing resources in accordance with the present invention;

FIG. 55 is a flowchart illustrating an example of retrieving distributedcomputed data in accordance with the present invention;

FIG. 56 is a flowchart illustrating an example of load-balancingdistributed computing resources in accordance with the presentinvention;

FIG. 57 is a flowchart illustrating an example of transforming a taskinto sub-tasks in accordance with the present invention;

FIG. 58A is a diagram of another example of error encoding and slicingprocessing of dispersed error encoding in accordance with the presentinvention;

FIG. 58B is a diagram of an example of transforming data blocks inaccordance with the present invention;

FIG. 58C is a schematic block diagram of another embodiment of adistributed computing system in accordance with the present invention;

FIG. 58D is a flowchart illustrating an example of transforming data inaccordance with the present invention;

FIG. 58E is a schematic block diagram of another embodiment of adistributed computing system in accordance with the present invention;

FIG. 58F is a flowchart illustrating another example of transformingdata in accordance with the present invention;

FIG. 59 is a flowchart illustrating another example of transformingstore data in accordance with the present invention;

FIG. 60A is a diagram illustrating an example of non-sequential datasegment storage mapping in accordance with the present invention;

FIG. 60B is a diagram illustrating an example of sequential data segmentstorage mapping in accordance with the present invention;

FIG. 60C is a schematic block diagram of an embodiment of a distributedstorage network in accordance with the present invention;

FIG. 60D is a flowchart illustrating another example of storing data inaccordance with the present invention;

FIG. 60E is a schematic block diagram of another embodiment of adistributed storage network in accordance with the present invention;

FIG. 60F is a flowchart illustrating another example of storing data inaccordance with the present invention;

FIG. 61A is a schematic block diagram of another embodiment of adistributed storage network in accordance with the present invention;

FIG. 61B is a flowchart illustrating an example of retrieving data inaccordance with the present invention; and

FIG. 62 is a flowchart illustrating an example of upgrading software inaccordance with the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a distributedcomputing system 10 that includes a user device 12 and/or a user device14, a distributed storage and/or task (DST) processing unit 16, adistributed storage and/or task network (DSTN) managing unit 18, a DSTintegrity processing unit 20, and a distributed storage and/or tasknetwork (DSTN) module 22. The components of the distributed computingsystem 10 are coupled via a network 24, which may include one or morewireless and/or wire lined communication systems; one or more privateintranet systems and/or public internet systems; and/or one or morelocal area networks (LAN) and/or wide area networks (WAN).

The DSTN module 22 includes a plurality of distributed storage and/ortask (DST) execution units 36 that may be located at geographicallydifferent sites (e.g., one in Chicago, one in Milwaukee, etc.). Each ofthe DST execution units is operable to store dispersed error encodeddata and/or to execute, in a distributed manner, one or more tasks ondata. The tasks may be a simple function (e.g., a mathematical function,a logic function, an identify function, a find function, a search enginefunction, a replace function, etc.), a complex function (e.g.,compression, human and/or computer language translation, text-to-voiceconversion, voice-to-text conversion, etc.), multiple simple and/orcomplex functions, one or more algorithms, one or more applications,etc.

Each of the user devices 12-14, the DST processing unit 16, the DSTNmanaging unit 18, and the DST integrity processing unit 20 include acomputing core 26 and may be a portable computing device and/or a fixedcomputing device. A portable computing device may be a social networkingdevice, a gaming device, a cell phone, a smart phone, a personal digitalassistant, a digital music player, a digital video player, a laptopcomputer, a handheld computer, a tablet, a video game controller, and/orany other portable device that includes a computing core. A fixedcomputing device may be a personal computer (PC), a computer server, acable set-top box, a satellite receiver, a television set, a printer, afax machine, home entertainment equipment, a video game console, and/orany type of home or office computing equipment. User device 12 and DSTprocessing unit 16 are configured to include a DST client module 34.

With respect to interfaces, each interface 30, 32, and 33 includessoftware and/or hardware to support one or more communication links viathe network 24 indirectly and/or directly. For example, interface 30supports a communication link (e.g., wired, wireless, direct, via a LAN,via the network 24, etc.) between user device 14 and the DST processingunit 16. As another example, interface 32 supports communication links(e.g., a wired connection, a wireless connection, a LAN connection,and/or any other type of connection to/from the network 24) between userdevice 12 and the DSTN module 22 and between the DST processing unit 16and the DSTN module 22. As yet another example, interface 33 supports acommunication link for each of the DSTN managing unit 18 and DSTintegrity processing unit 20 to the network 24.

The distributed computing system 10 is operable to support dispersedstorage (DS) error encoded data storage and retrieval, to supportdistributed task processing on received data, and/or to supportdistributed task processing on stored data. In general and with respectto DS error encoded data storage and retrieval, the distributedcomputing system 10 supports three primary operations: storagemanagement, data storage and retrieval (an example of which will bediscussed with reference to FIGS. 20-26), and data storage integrityverification. In accordance with these three primary functions, data canbe encoded, distributedly stored in physically different locations, andsubsequently retrieved in a reliable and secure manner. Such a system istolerant of a significant number of failures (e.g., up to a failurelevel, which may be greater than or equal to a pillar width minus adecode threshold minus one) that may result from individual storagedevice failures and/or network equipment failures without loss of dataand without the need for a redundant or backup copy. Further, the systemallows the data to be stored for an indefinite period of time withoutdata loss and does so in a secure manner (e.g., the system is veryresistant to attempts at hacking the data).

The second primary function (i.e., distributed data storage andretrieval) begins and ends with a user device 12-14. For instance, if asecond type of user device 14 has data 40 to store in the DSTN module22, it sends the data 40 to the DST processing unit 16 via its interface30. The interface 30 functions to mimic a conventional operating system(OS) file system interface (e.g., network file system (NFS), flash filesystem (FFS), disk file system (DFS), file transfer protocol (FTP),web-based distributed authoring and versioning (WebDAV), etc.) and/or ablock memory interface (e.g., small computer system interface (SCSI),internet small computer system interface (iSCSI), etc.). In addition,the interface 30 may attach a user identification code (ID) to the data40.

To support storage management, the DSTN managing unit 18 performs DSmanagement services. One such DS management service includes the DSTNmanaging unit 18 establishing distributed data storage parameters (e.g.,vault creation, distributed storage parameters, security parameters,billing information, user profile information, etc.) for a user device12-14 individually or as part of a group of user devices. For example,the DSTN managing unit 18 coordinates creation of a vault (e.g., avirtual memory block) within memory of the DSTN module 22 for a userdevice, a group of devices, or for public access and establishes pervault dispersed storage (DS) error encoding parameters for a vault. TheDSTN managing unit 18 may facilitate storage of DS error encodingparameters for each vault of a plurality of vaults by updating registryinformation for the distributed computing system 10. The facilitatingincludes storing updated registry information in one or more of the DSTNmodule 22, the user device 12, the DST processing unit 16, and the DSTintegrity processing unit 20.

The DS error encoding parameters (e.g., or dispersed storage errorcoding parameters) include data segmenting information (e.g., how manysegments data (e.g., a file, a group of files, a data block, etc.) isdivided into), segment security information (e.g., per segmentencryption, compression, integrity checksum, etc.), error codinginformation (e.g., pillar width, decode threshold, read threshold, writethreshold, etc.), slicing information (e.g., the number of encoded dataslices that will be created for each data segment); and slice securityinformation (e.g., per encoded data slice encryption, compression,integrity checksum, etc.).

The DSTN managing unit 18 creates and stores user profile information(e.g., an access control list (ACL)) in local memory and/or withinmemory of the DSTN module 22. The user profile information includesauthentication information, permissions, and/or the security parameters.The security parameters may include encryption/decryption scheme, one ormore encryption keys, key generation scheme, and/or dataencoding/decoding scheme.

The DSTN managing unit 18 creates billing information for a particularuser, a user group, a vault access, public vault access, etc. Forinstance, the DSTN managing unit 18 tracks the number of times a useraccesses a private vault and/or public vaults, which can be used togenerate a per-access billing information. In another instance, the DSTNmanaging unit 18 tracks the amount of data stored and/or retrieved by auser device and/or a user group, which can be used to generate aper-data-amount billing information.

Another DS management service includes the DSTN managing unit 18performing network operations, network administration, and/or networkmaintenance. Network operations includes authenticating user dataallocation requests (e.g., read and/or write requests), managingcreation of vaults, establishing authentication credentials for userdevices, adding/deleting components (e.g., user devices, DST executionunits, and/or DST processing units) from the distributed computingsystem 10, and/or establishing authentication credentials for DSTexecution units 36. Network administration includes monitoring devicesand/or units for failures, maintaining vault information, determiningdevice and/or unit activation status, determining device and/or unitloading, and/or determining any other system level operation thataffects the performance level of the system 10. Network maintenanceincludes facilitating replacing, upgrading, repairing, and/or expandinga device and/or unit of the system 10.

To support data storage integrity verification within the distributedcomputing system 10, the DST integrity processing unit 20 performsrebuilding of ‘bad’ or missing encoded data slices. At a high level, theDST integrity processing unit 20 performs rebuilding by periodicallyattempting to retrieve/list encoded data slices, and/or slice names ofthe encoded data slices, from the DSTN module 22. For retrieved encodedslices, they are checked for errors due to data corruption, outdatedversion, etc. If a slice includes an error, it is flagged as a ‘bad’slice. For encoded data slices that were not received and/or not listed,they are flagged as missing slices. Bad and/or missing slices aresubsequently rebuilt using other retrieved encoded data slices that aredeemed to be good slices to produce rebuilt slices. The rebuilt slicesare stored in memory of the DSTN module 22. Note that the DST integrityprocessing unit 20 may be a separate unit as shown, it may be includedin the DSTN module 22, it may be included in the DST processing unit 16,and/or distributed among the DST execution units 36.

To support distributed task processing on received data, the distributedcomputing system 10 has two primary operations: DST (distributed storageand/or task processing) management and DST execution on received data(an example of which will be discussed with reference to FIGS. 3-19).With respect to the storage portion of the DST management, the DSTNmanaging unit 18 functions as previously described. With respect to thetasking processing of the DST management, the DSTN managing unit 18performs distributed task processing (DTP) management services. One suchDTP management service includes the DSTN managing unit 18 establishingDTP parameters (e.g., user-vault affiliation information, billinginformation, user-task information, etc.) for a user device 12-14individually or as part of a group of user devices.

Another DTP management service includes the DSTN managing unit 18performing DTP network operations, network administration (which isessentially the same as described above), and/or network maintenance(which is essentially the same as described above). Network operationsinclude, but are not limited to, authenticating user task processingrequests (e.g., valid request, valid user, etc.), authenticating resultsand/or partial results, establishing DTP authentication credentials foruser devices, adding/deleting components (e.g., user devices, DSTexecution units, and/or DST processing units) from the distributedcomputing system, and/or establishing DTP authentication credentials forDST execution units.

To support distributed task processing on stored data, the distributedcomputing system 10 has two primary operations: DST (distributed storageand/or task) management and DST execution on stored data. With respectto the DST execution on stored data, if the second type of user device14 has a task request 38 for execution by the DSTN module 22, it sendsthe task request 38 to the DST processing unit 16 via its interface 30.An example of DST execution on stored data will be discussed in greaterdetail with reference to FIGS. 27-39. With respect to the DSTmanagement, it is substantially similar to the DST management to supportdistributed task processing on received data.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (IO)controller 56, a peripheral component interconnect (PCI) interface 58,an IO interface module 60, at least one IO device interface module 62, aread only memory (ROM) basic input output system (BIOS) 64, and one ormore memory interface modules. The one or more memory interfacemodule(s) includes one or more of a universal serial bus (USB) interfacemodule 66, a host bus adapter (HBA) interface module 68, a networkinterface module 70, a flash interface module 72, a hard drive interfacemodule 74, and a DSTN interface module 76.

The DSTN interface module 76 functions to mimic a conventional operatingsystem (OS) file system interface (e.g., network file system (NFS),flash file system (FFS), disk file system (DFS), file transfer protocol(FTP), web-based distributed authoring and versioning (WebDAV), etc.)and/or a block memory interface (e.g., small computer system interface(SCSI), internet small computer system interface (iSCSI), etc.). TheDSTN interface module 76 and/or the network interface module 70 mayfunction as the interface 30 of the user device 14 of FIG. 1. Furthernote that the IO device interface module 62 and/or the memory interfacemodules may be collectively or individually referred to as 10 ports.

FIG. 3 is a diagram of an example of the distributed computing systemperforming a distributed storage and task processing operation. Thedistributed computing system includes a DST (distributed storage and/ortask) client module 34 (which may be in user device 14 and/or in DSTprocessing unit 16 of FIG. 1), a network 24, a plurality of DSTexecution units 1-n that includes two or more DST execution units 36 ofFIG. 1 (which form at least a portion of DSTN module 22 of FIG. 1), aDST managing module (not shown), and a DST integrity verification module(not shown). The DST client module 34 includes an outbound DSTprocessing section 80 and an inbound DST processing section 82. Each ofthe DST execution units 1-n includes a controller 86, a processingmodule 84, memory 88, a DT (distributed task) execution module 90, and aDST client module 34.

In an example of operation, the DST client module 34 receives data 92and one or more tasks 94 to be performed upon the data 92. The data 92may be of any size and of any content, where, due to the size (e.g.,greater than a few Terra-Bytes), the content (e.g., secure data, etc.),and/or task(s) (e.g., MIPS intensive), distributed processing of thetask(s) on the data is desired. For example, the data 92 may be one ormore digital books, a copy of a company's emails, a large-scale Internetsearch, a video security file, one or more entertainment video files(e.g., television programs, movies, etc.), data files, and/or any otherlarge amount of data (e.g., greater than a few Terra-Bytes).

Within the DST client module 34, the outbound DST processing section 80receives the data 92 and the task(s) 94. The outbound DST processingsection 80 processes the data 92 to produce slice groupings 96. As anexample of such processing, the outbound DST processing section 80partitions the data 92 into a plurality of data partitions. For eachdata partition, the outbound DST processing section 80 dispersed storage(DS) error encodes the data partition to produce encoded data slices andgroups the encoded data slices into a slice grouping 96. In addition,the outbound DST processing section 80 partitions the task 94 intopartial tasks 98, where the number of partial tasks 98 may correspond tothe number of slice groupings 96.

The outbound DST processing section 80 then sends, via the network 24,the slice groupings 96 and the partial tasks 98 to the DST executionunits 1-n of the DSTN module 22 of FIG. 1. For example, the outbound DSTprocessing section 80 sends slice group 1 and partial task 1 to DSTexecution unit 1. As another example, the outbound DST processingsection 80 sends slice group #n and partial task #n to DST executionunit #n.

Each DST execution unit 36 performs its partial task 98 upon its slicegroup 96 to produce partial results 102. For example, DST execution unit#1 performs partial task #1 on slice group #1 to produce a partialresult #1, for results. As a more specific example, slice group #1corresponds to a data partition of a series of digital books and thepartial task #1 corresponds to searching for specific phrases, recordingwhere the phrase is found, and establishing a phrase count. In this morespecific example, the partial result #1 includes information as to wherethe phrase was found and includes the phrase count.

Upon completion of generating their respective partial results 102, theDST execution units 36 send, via the network 24, their partial results102 to the inbound DST processing section 82 of the DST client module34. The inbound DST processing section 82 processes the received partialresults 102 to produce a result 104. Continuing with the specificexample of the preceding paragraph, the inbound DST processing section82 combines the phrase count from each of the DST execution units 36 toproduce a total phrase count. In addition, the inbound DST processingsection 82 combines the ‘where the phrase was found’ information fromeach of the DST execution units 36 within their respective datapartitions to produce ‘where the phrase was found’ information for theseries of digital books.

In another example of operation, the DST client module 34 requestsretrieval of stored data within the memory of the DST execution units 36(e.g., memory of the DSTN module). In this example, the task 94 isretrieve data stored in the memory of the DSTN module. Accordingly, theoutbound DST processing section 80 converts the task 94 into a pluralityof partial tasks 98 and sends the partial tasks 98 to the respective DSTexecution units 1-n.

In response to the partial task 98 of retrieving stored data, a DSTexecution unit 36 identifies the corresponding encoded data slices 100and retrieves them. For example, DST execution unit #1 receives partialtask #1 and retrieves, in response thereto, retrieved slices #1. The DSTexecution units 36 send their respective retrieved slices 100 to theinbound DST processing section 82 via the network 24.

The inbound DST processing section 82 converts the retrieved slices 100into data 92. For example, the inbound DST processing section 82de-groups the retrieved slices 100 to produce encoded slices per datapartition. The inbound DST processing section 82 then DS error decodesthe encoded slices per data partition to produce data partitions. Theinbound DST processing section 82 de-partitions the data partitions torecapture the data 92.

FIG. 4 is a schematic block diagram of an embodiment of an outbounddistributed storage and/or task (DST) processing section 80 of a DSTclient module 34 FIG. 1 coupled to a DSTN module 22 of a FIG. 1 (e.g., aplurality of n DST execution units 36) via a network 24. The outboundDST processing section 80 includes a data partitioning module 110, adispersed storage (DS) error encoding module 112, a grouping selectormodule 114, a control module 116, and a distributed task control module118.

In an example of operation, the data partitioning module 110 partitionsdata 92 into a plurality of data partitions 120. The number ofpartitions and the size of the partitions may be selected by the controlmodule 116 via control 160 based on the data 92 (e.g., its size, itscontent, etc.), a corresponding task 94 to be performed (e.g., simple,complex, single step, multiple steps, etc.), DS encoding parameters(e.g., pillar width, decode threshold, write threshold, segment securityparameters, slice security parameters, etc.), capabilities of the DSTexecution units 36 (e.g., processing resources, availability ofprocessing recourses, etc.), and/or as may be inputted by a user, systemadministrator, or other operator (human or automated). For example, thedata partitioning module 110 partitions the data 92 (e.g., 100Terra-Bytes) into 100,000 data segments, each being 1 Giga-Byte in size.Alternatively, the data partitioning module 110 partitions the data 92into a plurality of data segments, where some of data segments are of adifferent size, are of the same size, or a combination thereof.

The DS error encoding module 112 receives the data partitions 120 in aserial manner, a parallel manner, and/or a combination thereof. For eachdata partition 120, the DS error encoding module 112 DS error encodesthe data partition 120 in accordance with control information 160 fromthe control module 116 to produce encoded data slices 122. The DS errorencoding includes segmenting the data partition into data segments,segment security processing (e.g., encryption, compression,watermarking, integrity check (e.g., CRC), etc.), error encoding,slicing, and/or per slice security processing (e.g., encryption,compression, watermarking, integrity check (e.g., CRC), etc.). Thecontrol information 160 indicates which steps of the DS error encodingare active for a given data partition and, for active steps, indicatesthe parameters for the step. For example, the control information 160indicates that the error encoding is active and includes error encodingparameters (e.g., pillar width, decode threshold, write threshold, readthreshold, type of error encoding, etc.).

The grouping selector module 114 groups the encoded slices 122 of a datapartition into a set of slice groupings 96. The number of slicegroupings corresponds to the number of DST execution units 36 identifiedfor a particular task 94. For example, if five DST execution units 36are identified for the particular task 94, the grouping selector modulegroups the encoded slices 122 of a data partition into five slicegroupings 96. The grouping selector module 114 outputs the slicegroupings 96 to the corresponding DST execution units 36 via the network24.

The distributed task control module 118 receives the task 94 andconverts the task 94 into a set of partial tasks 98. For example, thedistributed task control module 118 receives a task to find where in thedata (e.g., a series of books) a phrase occurs and a total count of thephrase usage in the data. In this example, the distributed task controlmodule 118 replicates the task 94 for each DST execution unit 36 toproduce the partial tasks 98. In another example, the distributed taskcontrol module 118 receives a task to find where in the data a firstphrase occurs, where in the data a second phrase occurs, and a totalcount for each phrase usage in the data. In this example, thedistributed task control module 118 generates a first set of partialtasks 98 for finding and counting the first phrase and a second set ofpartial tasks for finding and counting the second phrase. Thedistributed task control module 118 sends respective first and/or secondpartial tasks 98 to each DST execution unit 36.

FIG. 5 is a logic diagram of an example of a method for outbounddistributed storage and task (DST) processing that begins at step 126where a DST client module receives data and one or more correspondingtasks. The method continues at step 128 where the DST client moduledetermines a number of DST units to support the task for one or moredata partitions. For example, the DST client module may determine thenumber of DST units to support the task based on the size of the data,the requested task, the content of the data, a predetermined number(e.g., user indicated, system administrator determined, etc.), availableDST units, capability of the DST units, and/or any other factorregarding distributed task processing of the data. The DST client modulemay select the same DST units for each data partition, may selectdifferent DST units for the data partitions, or a combination thereof.

The method continues at step 130 where the DST client module determinesprocessing parameters of the data based on the number of DST unitsselected for distributed task processing. The processing parametersinclude data partitioning information, DS encoding parameters, and/orslice grouping information. The data partitioning information includes anumber of data partitions, size of each data partition, and/ororganization of the data partitions (e.g., number of data blocks in apartition, the size of the data blocks, and arrangement of the datablocks). The DS encoding parameters include segmenting information,segment security information, error encoding information (e.g.,dispersed storage error encoding function parameters including one ormore of pillar width, decode threshold, write threshold, read threshold,generator matrix), slicing information, and/or per slice securityinformation. The slice grouping information includes informationregarding how to arrange the encoded data slices into groups for theselected DST units. As a specific example, if, the DST client moduledetermines that five DST units are needed to support the task, then itdetermines that the error encoding parameters include a pillar width offive and a decode threshold of three.

The method continues at step 132 where the DST client module determinestask partitioning information (e.g., how to partition the tasks) basedon the selected DST units and data processing parameters. The dataprocessing parameters include the processing parameters and DST unitcapability information. The DST unit capability information includes thenumber of DT (distributed task) execution units, execution capabilitiesof each DT execution unit (e.g., MIPS capabilities, processing resources(e.g., quantity and capability of microprocessors, CPUs, digital signalprocessors, co-processor, microcontrollers, arithmetic logic circuitry,and/or any other analog and/or digital processing circuitry),availability of the processing resources, memory information (e.g.,type, size, availability, etc.)), and/or any information germane toexecuting one or more tasks.

The method continues at step 134 where the DST client module processesthe data in accordance with the processing parameters to produce slicegroupings. The method continues at step 136 where the DST client modulepartitions the task based on the task partitioning information toproduce a set of partial tasks. The method continues at step 138 wherethe DST client module sends the slice groupings and the correspondingpartial tasks to the selected DST units.

FIG. 6 is a schematic block diagram of an embodiment of the dispersedstorage (DS) error encoding module 112 of an outbound distributedstorage and task (DST) processing section. The DS error encoding module112 includes a segment processing module 142, a segment securityprocessing module 144, an error encoding module 146, a slicing module148, and a per slice security processing module 150. Each of thesemodules is coupled to a control module 116 to receive controlinformation 160 therefrom.

In an example of operation, the segment processing module 142 receives adata partition 120 from a data partitioning module and receivessegmenting information as the control information 160 from the controlmodule 116. The segmenting information indicates how the segmentprocessing module 142 is to segment the data partition 120. For example,the segmenting information indicates how many rows to segment the databased on a decode threshold of an error encoding scheme, indicates howmany columns to segment the data into based on a number and size of datablocks within the data partition 120, and indicates how many columns toinclude in a data segment 152. The segment processing module 142segments the data 120 into data segments 152 in accordance with thesegmenting information.

The segment security processing module 144, when enabled by the controlmodule 116, secures the data segments 152 based on segment securityinformation received as control information 160 from the control module116. The segment security information includes data compression,encryption, watermarking, integrity check (e.g., cyclic redundancy checkCRC), etc., and/or any other type of digital security. For example, whenthe segment security processing module 144 is enabled, it may compress adata segment 152, encrypt the compressed data segment, and generate aCRC value for the encrypted data segment to produce a secure datasegment 154. When the segment security processing module 144 is notenabled, it passes the data segments 152 to the error encoding module146 or is bypassed such that the data segments 152 are provided to theerror encoding module 146.

The error encoding module 146 encodes the secure data segments 154 inaccordance with error correction encoding parameters received as controlinformation 160 from the control module 116. The error correctionencoding parameters (e.g., also referred to as dispersed storage errorcoding parameters) include identifying an error correction encodingscheme (e.g., forward error correction algorithm, a Reed-Solomon basedalgorithm, an online coding algorithm, an information dispersalalgorithm, etc.), a pillar width, a decode threshold, a read threshold,a write threshold, etc. For example, the error correction encodingparameters identify a specific error correction encoding scheme,specifies a pillar width of five, and specifies a decode threshold ofthree. From these parameters, the error encoding module 146 encodes adata segment 154 to produce an encoded data segment 156.

The slicing module 148 slices the encoded data segment 156 in accordancewith the pillar width of the error correction encoding parametersreceived as control information 160. For example, if the pillar width isfive, the slicing module 148 slices an encoded data segment 156 into aset of five encoded data slices. As such, for a plurality of encodeddata segments 156 for a given data partition, the slicing module outputsa plurality of sets of encoded data slices 158.

The per slice security processing module 150, when enabled by thecontrol module 116, secures each encoded data slice 158 based on slicesecurity information received as control information 160 from thecontrol module 116. The slice security information includes datacompression, encryption, watermarking, integrity check (e.g., CRC),etc., and/or any other type of digital security. For example, when theper slice security processing module 150 is enabled, it compresses anencoded data slice 158, encrypts the compressed encoded data slice, andgenerates a CRC value for the encrypted encoded data slice to produce asecure encoded data slice 122. When the per slice security processingmodule 150 is not enabled, it passes the encoded data slices 158 or isbypassed such that the encoded data slices 158 are the output of the DSerror encoding module 112. Note that the control module 116 may beomitted and each module stores its own parameters.

FIG. 7 is a diagram of an example of a segment processing of a dispersedstorage (DS) error encoding module. In this example, a segmentprocessing module 142 receives a data partition 120 that includes 45data blocks (e.g., d1-d45) and receives segmenting information (i.e.,control information 160) from a control module. Each data block may beof the same size as other data blocks or of a different size. Inaddition, the size of each data block may be a few bytes to megabytes ofdata. As previously mentioned, the segmenting information indicates howmany rows to segment the data partition into, indicates how many columnsto segment the data partition into, and indicates how many columns toinclude in a data segment.

In this example, the decode threshold of the error encoding scheme isthree; as such the number of rows to divide the data partition into isthree. The number of columns for each row is set to 15, which is basedon the number and size of data blocks. The data blocks of the datapartition are arranged in rows and columns in a sequential order (i.e.,the first row includes the first 15 data blocks; the second row includesthe second 15 data blocks; and the third row includes the last 15 datablocks).

With the data blocks arranged into the desired sequential order, theyare divided into data segments based on the segmenting information. Inthis example, the data partition is divided into 8 data segments; thefirst 7 include 2 columns of three rows and the last includes 1 columnof three rows. Note that the first row of the 8 data segments is insequential order of the first 15 data blocks; the second row of the 8data segments in sequential order of the second 15 data blocks; and thethird row of the 8 data segments in sequential order of the last 15 datablocks. Note that the number of data blocks, the grouping of the datablocks into segments, and size of the data blocks may vary toaccommodate the desired distributed task processing function.

FIG. 8 is a diagram of an example of error encoding and slicingprocessing of the dispersed error encoding processing the data segmentsof FIG. 7. In this example, data segment 1 includes 3 rows with each rowbeing treated as one word for encoding. As such, data segment 1 includesthree words for encoding: word 1 including data blocks d1 and d2, word 2including data blocks d16 and d17, and word 3 including data blocks d31and d32. Each of data segments 2-7 includes three words where each wordincludes two data blocks. Data segment 8 includes three words where eachword includes a single data block (e.g., d15, d30, and d45).

In operation, an error encoding module 146 and a slicing module 148convert each data segment into a set of encoded data slices inaccordance with error correction encoding parameters as controlinformation 160. More specifically, when the error correction encodingparameters indicate a unity matrix Reed-Solomon based encodingalgorithm, 5 pillars, and decode threshold of 3, the first three encodeddata slices of the set of encoded data slices for a data segment aresubstantially similar to the corresponding word of the data segment. Forinstance, when the unity matrix Reed-Solomon based encoding algorithm isapplied to data segment 1, the content of the first encoded data slice(DS1_d1&2) of the first set of encoded data slices (e.g., correspondingto data segment 1) is substantially similar to content of the first word(e.g., d1 & d2); the content of the second encoded data slice(DS1_d16&17) of the first set of encoded data slices is substantiallysimilar to content of the second word (e.g., d16 & d17); and the contentof the third encoded data slice (DS1_d31&32) of the first set of encodeddata slices is substantially similar to content of the third word (e.g.,d31 & d32).

The content of the fourth and fifth encoded data slices (e.g., ES1_1 andES1_2) of the first set of encoded data slices include error correctiondata based on the first-third words of the first data segment. With suchan encoding and slicing scheme, retrieving any three of the five encodeddata slices allows the data segment to be accurately reconstructed.

The encoding and slicing of data segments 2-7 yield sets of encoded dataslices similar to the set of encoded data slices of data segment 1. Forinstance, the content of the first encoded data slice (DS2_d3&4) of thesecond set of encoded data slices (e.g., corresponding to data segment2) is substantially similar to content of the first word (e.g., d3 &d4); the content of the second encoded data slice (DS2_d18&19) of thesecond set of encoded data slices is substantially similar to content ofthe second word (e.g., d18 & d19); and the content of the third encodeddata slice (DS2_d33&34) of the second set of encoded data slices issubstantially similar to content of the third word (e.g., d33 & d34).The content of the fourth and fifth encoded data slices (e.g., ES1_1 andES1_2) of the second set of encoded data slices includes errorcorrection data based on the first-third words of the second datasegment.

FIG. 9 is a diagram of an example of grouping selection processing of anoutbound distributed storage and task (DST) processing in accordancewith group selection information as control information 160 from acontrol module. In this example, a grouping selector module 114organizes the encoded data slices into five slice groupings (e.g., onefor each DST execution unit of a distributed storage and task network(DSTN) module). As a specific example, the grouping selector module 114creates a first slice grouping for a DST execution unit #1, whichincludes first encoded slices of each of the sets of encoded slices. Assuch, the first DST execution unit receives encoded data slicescorresponding to data blocks 1-15 (e.g., encoded data slices ofcontiguous data).

The grouping selector module 114 also creates a second slice groupingfor a DST execution unit #2, which includes second encoded slices ofeach of the sets of encoded slices. As such, the second DST executionunit receives encoded data slices corresponding to data blocks 16-30.The grouping selector module 114 further creates a third slice groupingfor DST execution unit #3, which includes third encoded slices of eachof the sets of encoded slices. As such, the third DST execution unitreceives encoded data slices corresponding to data blocks 31-45.

The grouping selector module 114 creates a fourth slice grouping for DSTexecution unit #4, which includes fourth encoded slices of each of thesets of encoded slices. As such, the fourth DST execution unit receivesencoded data slices corresponding to first error encoding information(e.g., encoded data slices of error coding (EC) data). The groupingselector module 114 further creates a fifth slice grouping for DSTexecution unit #5, which includes fifth encoded slices of each of thesets of encoded slices. As such, the fifth DST execution unit receivesencoded data slices corresponding to second error encoding information.

FIG. 10 is a diagram of an example of converting data 92 into slicegroups that expands on the preceding figures. As shown, the data 92 ispartitioned in accordance with a partitioning function 164 into aplurality of data partitions (1-x, where x is an integer greater than4). Each data partition (or chunkset of data) is encoded and groupedinto slice groupings as previously discussed by an encoding and groupingfunction 166. For a given data partition, the slice groupings are sentto distributed storage and task (DST) execution units. From datapartition to data partition, the ordering of the slice groupings to theDST execution units may vary.

For example, the slice groupings of data partition #1 is sent to the DSTexecution units such that the first DST execution receives first encodeddata slices of each of the sets of encoded data slices, whichcorresponds to a first continuous data chunk of the first data partition(e.g., refer to FIG. 9), a second DST execution receives second encodeddata slices of each of the sets of encoded data slices, whichcorresponds to a second continuous data chunk of the first datapartition, etc.

For the second data partition, the slice groupings may be sent to theDST execution units in a different order than it was done for the firstdata partition. For instance, the first slice grouping of the seconddata partition (e.g., slice group 2_1) is sent to the second DSTexecution unit; the second slice grouping of the second data partition(e.g., slice group 2_2) is sent to the third DST execution unit; thethird slice grouping of the second data partition (e.g., slice group2_3) is sent to the fourth DST execution unit; the fourth slice groupingof the second data partition (e.g., slice group 2_4, which includesfirst error coding information) is sent to the fifth DST execution unit;and the fifth slice grouping of the second data partition (e.g., slicegroup 2_5, which includes second error coding information) is sent tothe first DST execution unit.

The pattern of sending the slice groupings to the set of DST executionunits may vary in a predicted pattern, a random pattern, and/or acombination thereof from data partition to data partition. In addition,from data partition to data partition, the set of DST execution unitsmay change. For example, for the first data partition, DST executionunits 1-5 may be used; for the second data partition, DST executionunits 6-10 may be used; for the third data partition, DST executionunits 3-7 may be used; etc. As is also shown, the task is divided intopartial tasks that are sent to the DST execution units in conjunctionwith the slice groupings of the data partitions.

FIG. 11 is a schematic block diagram of an embodiment of a DST(distributed storage and/or task) execution unit that includes aninterface 169, a controller 86, memory 88, one or more DT (distributedtask) execution modules 90, and a DST client module 34. The memory 88 isof sufficient size to store a significant number of encoded data slices(e.g., thousands of slices to hundreds-of-millions of slices) and mayinclude one or more hard drives and/or one or more solid-state memorydevices (e.g., flash memory, DRAM, etc.).

In an example of storing a slice group, the DST execution modulereceives a slice grouping 96 (e.g., slice group #1) via interface 169.The slice grouping 96 includes, per partition, encoded data slices ofcontiguous data or encoded data slices of error coding (EC) data. Forslice group #1, the DST execution module receives encoded data slices ofcontiguous data for partitions #1 and #x (and potentially others between3 and x) and receives encoded data slices of EC data for partitions #2and #3 (and potentially others between 3 and x). Examples of encodeddata slices of contiguous data and encoded data slices of error coding(EC) data are discussed with reference to FIG. 9. The memory 88 storesthe encoded data slices of slice groupings 96 in accordance with memorycontrol information 174 it receives from the controller 86.

The controller 86 (e.g., a processing module, a CPU, etc.) generates thememory control information 174 based on a partial task(s) 98 anddistributed computing information (e.g., user information (e.g., userID, distributed computing permissions, data access permission, etc.),vault information (e.g., virtual memory assigned to user, user group,temporary storage for task processing, etc.), task validationinformation, etc.). For example, the controller 86 interprets thepartial task(s) 98 in light of the distributed computing information todetermine whether a requestor is authorized to perform the task 98, isauthorized to access the data, and/or is authorized to perform the taskon this particular data. When the requestor is authorized, thecontroller 86 determines, based on the task 98 and/or another input,whether the encoded data slices of the slice grouping 96 are to betemporarily stored or permanently stored. Based on the foregoing, thecontroller 86 generates the memory control information 174 to write theencoded data slices of the slice grouping 96 into the memory 88 and toindicate whether the slice grouping 96 is permanently stored ortemporarily stored.

With the slice grouping 96 stored in the memory 88, the controller 86facilitates execution of the partial task(s) 98. In an example, thecontroller 86 interprets the partial task 98 in light of thecapabilities of the DT execution module(s) 90. The capabilities includeone or more of MIPS capabilities, processing resources (e.g., quantityand capability of microprocessors, CPUs, digital signal processors,co-processor, microcontrollers, arithmetic logic circuitry, and/or anyother analog and/or digital processing circuitry), availability of theprocessing resources, etc. If the controller 86 determines that the DTexecution module(s) 90 have sufficient capabilities, it generates taskcontrol information 176.

The task control information 176 may be a generic instruction (e.g.,perform the task on the stored slice grouping) or a series ofoperational codes. In the former instance, the DT execution module 90includes a co-processor function specifically configured (fixed orprogrammed) to perform the desired task 98. In the latter instance, theDT execution module 90 includes a general processor topology where thecontroller stores an algorithm corresponding to the particular task 98.In this instance, the controller 86 provides the operational codes(e.g., assembly language, source code of a programming language, objectcode, etc.) of the algorithm to the DT execution module 90 forexecution.

Depending on the nature of the task 98, the DT execution module 90 maygenerate intermediate partial results 102 that are stored in the memory88 or in a cache memory (not shown) within the DT execution module 90.In either case, when the DT execution module 90 completes execution ofthe partial task 98, it outputs one or more partial results 102. Thepartial results 102 may also be stored in memory 88.

If, when the controller 86 is interpreting whether capabilities of theDT execution module(s) 90 can support the partial task 98, thecontroller 86 determines that the DT execution module(s) 90 cannotadequately support the task 98 (e.g., does not have the right resources,does not have sufficient available resources, available resources wouldbe too slow, etc.), it then determines whether the partial task 98should be fully offloaded or partially offloaded.

If the controller 86 determines that the partial task 98 should be fullyoffloaded, it generates DST control information 178 and provides it tothe DST client module 34. The DST control information 178 includes thepartial task 98, memory storage information regarding the slice grouping96, and distribution instructions. The distribution instructionsinstruct the DST client module 34 to divide the partial task 98 intosub-partial tasks 172, to divide the slice grouping 96 into sub-slicegroupings 170, and identify other DST execution units. The DST clientmodule 34 functions in a similar manner as the DST client module 34 ofFIGS. 3-10 to produce the sub-partial tasks 172 and the sub-slicegroupings 170 in accordance with the distribution instructions.

The DST client module 34 receives DST feedback 168 (e.g., sub-partialresults), via the interface 169, from the DST execution units to whichthe task was offloaded. The DST client module 34 provides thesub-partial results to the DST execution unit, which processes thesub-partial results to produce the partial result(s) 102.

If the controller 86 determines that the partial task 98 should bepartially offloaded, it determines what portion of the task 98 and/orslice grouping 96 should be processed locally and what should beoffloaded. For the portion that is being locally processed, thecontroller 86 generates task control information 176 as previouslydiscussed. For the portion that is being offloaded, the controller 86generates DST control information 178 as previously discussed.

When the DST client module 34 receives DST feedback 168 (e.g.,sub-partial results) from the DST executions units to which a portion ofthe task was offloaded, it provides the sub-partial results to the DTexecution module 90. The DT execution module 90 processes thesub-partial results with the sub-partial results it created to producethe partial result(s) 102.

The memory 88 may be further utilized to retrieve one or more of storedslices 100, stored results 104, partial results 102 when the DTexecution module 90 stores partial results 102 and/or results 104 in thememory 88. For example, when the partial task 98 includes a retrievalrequest, the controller 86 outputs the memory control 174 to the memory88 to facilitate retrieval of slices 100 and/or results 104.

FIG. 12 is a schematic block diagram of an example of operation of adistributed storage and task (DST) execution unit storing encoded dataslices and executing a task thereon. To store the encoded data slices ofa partition 1 of slice grouping 1, a controller 86 generates writecommands as memory control information 174 such that the encoded slicesare stored in desired locations (e.g., permanent or temporary) withinmemory 88.

Once the encoded slices are stored, the controller 86 provides taskcontrol information 176 to a distributed task (DT) execution module 90.As a first step of executing the task in accordance with the taskcontrol information 176, the DT execution module 90 retrieves theencoded slices from memory 88. The DT execution module 90 thenreconstructs contiguous data blocks of a data partition. As shown forthis example, reconstructed contiguous data blocks of data partition 1include data blocks 1-15 (e.g., d1-d15).

With the contiguous data blocks reconstructed, the DT execution module90 performs the task on the reconstructed contiguous data blocks. Forexample, the task may be to search the reconstructed contiguous datablocks for a particular word or phrase, identify where in thereconstructed contiguous data blocks the particular word or phraseoccurred, and/or count the occurrences of the particular word or phraseon the reconstructed contiguous data blocks. The DST execution unitcontinues in a similar manner for the encoded data slices of otherpartitions in slice grouping 1. Note that with using the unity matrixerror encoding scheme previously discussed, if the encoded data slicesof contiguous data are uncorrupted, the decoding of them is a relativelystraightforward process of extracting the data.

If, however, an encoded data slice of contiguous data is corrupted (ormissing), it can be rebuilt by accessing other DST execution units thatare storing the other encoded data slices of the set of encoded dataslices of the corrupted encoded data slice. In this instance, the DSTexecution unit having the corrupted encoded data slices retrieves atleast three encoded data slices (of contiguous data and of error codingdata) in the set from the other DST execution units (recall for thisexample, the pillar width is 5 and the decode threshold is 3). The DSTexecution unit decodes the retrieved data slices using the DS errorencoding parameters to recapture the corresponding data segment. The DSTexecution unit then re-encodes the data segment using the DS errorencoding parameters to rebuild the corrupted encoded data slice. Oncethe encoded data slice is rebuilt, the DST execution unit functions aspreviously described.

FIG. 13 is a schematic block diagram of an embodiment of an inbounddistributed storage and/or task (DST) processing section 82 of a DSTclient module coupled to DST execution units of a distributed storageand task network (DSTN) module via a network 24. The inbound DSTprocessing section 82 includes a de-grouping module 180, a DS (dispersedstorage) error decoding module 182, a data de-partitioning module 184, acontrol module 186, and a distributed task control module 188. Note thatthe control module 186 and/or the distributed task control module 188may be separate modules from corresponding ones of outbound DSTprocessing section or may be the same modules.

In an example of operation, the DST execution units have completedexecution of corresponding partial tasks on the corresponding slicegroupings to produce partial results 102. The inbound DST processingsection 82 receives the partial results 102 via the distributed taskcontrol module 188. The inbound DST processing section 82 then processesthe partial results 102 to produce a final result, or results 104. Forexample, if the task was to find a specific word or phrase within data,the partial results 102 indicate where in each of the prescribedportions of the data the corresponding DST execution units found thespecific word or phrase. The distributed task control module 188combines the individual partial results 102 for the correspondingportions of the data into a final result 104 for the data as a whole.

In another example of operation, the inbound DST processing section 82is retrieving stored data from the DST execution units (i.e., the DSTNmodule). In this example, the DST execution units output encoded dataslices 100 corresponding to the data retrieval requests. The de-groupingmodule 180 receives retrieved slices 100 and de-groups them to produceencoded data slices per data partition 122. The DS error decoding module182 decodes, in accordance with DS error encoding parameters, theencoded data slices per data partition 122 to produce data partitions120.

The data de-partitioning module 184 combines the data partitions 120into the data 92. The control module 186 controls the conversion ofretrieved slices 100 into the data 92 using control signals 190 to eachof the modules. For instance, the control module 186 providesde-grouping information to the de-grouping module 180, provides the DSerror encoding parameters to the DS error decoding module 182, andprovides de-partitioning information to the data de-partitioning module184.

FIG. 14 is a logic diagram of an example of a method that is executableby distributed storage and task (DST) client module regarding inboundDST processing. The method begins at step 194 where the DST clientmodule receives partial results. The method continues at step 196 wherethe DST client module retrieves the task corresponding to the partialresults. For example, the partial results include header informationthat identifies the requesting entity, which correlates to the requestedtask.

The method continues at step 198 where the DST client module determinesresult processing information based on the task. For example, if thetask were to identify a particular word or phrase within the data, theresult processing information would indicate to aggregate the partialresults for the corresponding portions of the data to produce the finalresult. As another example, if the task were to count the occurrences ofa particular word or phrase within the data, the results of processinginformation would indicate to add the partial results to produce thefinal results. The method continues at step 200 where the DST clientmodule processes the partial results in accordance with the resultprocessing information to produce the final result, or results.

FIG. 15 is a diagram of an example of de-grouping selection processingof an inbound distributed storage and task (DST) processing section of aDST client module. In general, this is an inverse process of thegrouping module of the outbound DST processing section of FIG. 9.Accordingly, for each data partition (e.g., partition #1), thede-grouping module retrieves the corresponding slice grouping from theDST execution units (EU) (e.g., DST 1-5).

As shown, DST execution unit #1 provides a first slice grouping, whichincludes the first encoded slices of each of the sets of encoded slices(e.g., encoded data slices of contiguous data of data blocks 1-15); DSTexecution unit #2 provides a second slice grouping, which includes thesecond encoded slices of each of the sets of encoded slices (e.g.,encoded data slices of contiguous data of data blocks 16-30); DSTexecution unit #3 provides a third slice grouping, which includes thethird encoded slices of each of the sets of encoded slices (e.g.,encoded data slices of contiguous data of data blocks 31-45); DSTexecution unit #4 provides a fourth slice grouping, which includes thefourth encoded slices of each of the sets of encoded slices (e.g., firstencoded data slices of error coding (EC) data); and DST execution unit#5 provides a fifth slice grouping, which includes the fifth encodedslices of each of the sets of encoded slices (e.g., first encoded dataslices of error coding (EC) data).

The de-grouping module de-groups the slice groupings (e.g., receivedslices 100) using a de-grouping selector 180 controlled by a controlsignal 190 as shown in the example to produce a plurality of sets ofencoded data slices (e.g., retrieved slices for a partition into sets ofslices 122). Each set corresponding to a data segment of the datapartition.

FIG. 16 is a schematic block diagram of an embodiment of a dispersedstorage (DS) error decoding module 182 of an inbound distributed storageand task (DST) processing section. The DS error decoding module 182includes an inverse per slice security processing module 202, ade-slicing module 204, an error decoding module 206, an inverse segmentsecurity module 208, a de-segmenting processing module 210, and acontrol module 186.

In an example of operation, the inverse per slice security processingmodule 202, when enabled by the control module 186, unsecures eachencoded data slice 122 based on slice de-security information receivedas control information 190 (e.g., the compliment of the slice securityinformation discussed with reference to FIG. 6) received from thecontrol module 186. The slice security information includes datadecompression, decryption, de-watermarking, integrity check (e.g., CRC)verification, etc., and/or any other type of digital security. Forexample, when the inverse per slice security processing module 202 isenabled, it verifies integrity information (e.g., a CRC value) of eachencoded data slice 122, it decrypts each verified encoded data slice,and decompresses each decrypted encoded data slice to produce sliceencoded data 158. When the inverse per slice security processing module202 is not enabled, it passes the encoded data slices 122 as the slicedencoded data 158 or is bypassed such that the retrieved encoded dataslices 122 are provided as the sliced encoded data 158.

The de-slicing module 204 de-slices the sliced encoded data 158 intoencoded data segments 156 in accordance with a pillar width of the errorcorrection encoding parameters received as control information 190 fromthe control module 186. For example, if the pillar width is five, thede-slicing module 204 de-slices a set of five encoded data slices intoan encoded data segment 156. The error decoding module 206 decodes theencoded data segments 156 in accordance with error correction decodingparameters received as control information 190 from the control module186 to produce secure data segments 154. The error correction decodingparameters include identifying an error correction encoding scheme(e.g., forward error correction algorithm, a Reed-Solomon basedalgorithm, an information dispersal algorithm, etc.), a pillar width, adecode threshold, a read threshold, a write threshold, etc. For example,the error correction decoding parameters identify a specific errorcorrection encoding scheme, specify a pillar width of five, and specifya decode threshold of three.

The inverse segment security processing module 208, when enabled by thecontrol module 186, unsecures the secured data segments 154 based onsegment security information received as control information 190 fromthe control module 186. The segment security information includes datadecompression, decryption, de-watermarking, integrity check (e.g., CRC),etc., verification, and/or any other type of digital security. Forexample, when the inverse segment security processing module 208 isenabled, it verifies integrity information (e.g., a CRC value) of eachsecure data segment 154, it decrypts each verified secured data segment,and decompresses each decrypted secure data segment to produce a datasegment 152. When the inverse segment security processing module 208 isnot enabled, it passes the decoded data segment 154 as the data segment152 or is bypassed.

The de-segment processing module 210 receives the data segments 152 andreceives de-segmenting information as control information 190 from thecontrol module 186. The de-segmenting information indicates how thede-segment processing module 210 is to de-segment the data segments 152into a data partition 120. For example, the de-segmenting informationindicates how the rows and columns of data segments are to be rearrangedto yield the data partition 120.

FIG. 17 is a diagram of an example of de-slicing and error decodingprocessing of a dispersed error decoding module. A de-slicing module 204receives at least a decode threshold number of encoded data slices 158for each data segment in accordance with control information 190 andprovides encoded data 156. In this example, a decode threshold is three.As such, each set of encoded data slices 158 is shown to have threeencoded data slices per data segment. The de-slicing module 204 mayreceive three encoded data slices per data segment because an associateddistributed storage and task (DST) client module requested retrievingonly three encoded data slices per segment or selected three of theretrieved encoded data slices per data segment. As shown, which is basedon the unity matrix encoding previously discussed with reference to FIG.8, an encoded data slice may be a data-based encoded data slice (e.g.,DS1_d1&d2) or an error code based encoded data slice (e.g., ES3_1).

An error decoding module 206 decodes the encoded data 156 of each datasegment in accordance with the error correction decoding parameters ofcontrol information 190 to produce secured segments 154. In thisexample, data segment 1 includes 3 rows with each row being treated asone word for encoding. As such, data segment 1 includes three words:word 1 including data blocks d1 and d2, word 2 including data blocks d16and d17, and word 3 including data blocks d31 and d32. Each of datasegments 2-7 includes three words where each word includes two datablocks. Data segment 8 includes three words where each word includes asingle data block (e.g., d15, d30, and d45).

FIG. 18 is a diagram of an example of de-segment processing of aninbound distributed storage and task (DST) processing. In this example,a de-segment processing module 210 receives data segments 152 (e.g.,1-8) and rearranges the data blocks of the data segments into rows andcolumns in accordance with de-segmenting information of controlinformation 190 to produce a data partition 120. Note that the number ofrows is based on the decode threshold (e.g., 3 in this specific example)and the number of columns is based on the number and size of the datablocks.

The de-segmenting module 210 converts the rows and columns of datablocks into the data partition 120. Note that each data block may be ofthe same size as other data blocks or of a different size. In addition,the size of each data block may be a few bytes to megabytes of data.

FIG. 19 is a diagram of an example of converting slice groups into data92 within an inbound distributed storage and task (DST) processingsection. As shown, the data 92 is reconstructed from a plurality of datapartitions (1-x, where x is an integer greater than 4). Each datapartition (or chunk set of data) is decoded and re-grouped using ade-grouping and decoding function 212 and a de-partition function 214from slice groupings as previously discussed. For a given datapartition, the slice groupings (e.g., at least a decode threshold perdata segment of encoded data slices) are received from DST executionunits. From data partition to data partition, the ordering of the slicegroupings received from the DST execution units may vary as discussedwith reference to FIG. 10.

FIG. 20 is a diagram of an example of a distributed storage and/orretrieval within the distributed computing system. The distributedcomputing system includes a plurality of distributed storage and/or task(DST) processing client modules 34 (one shown) coupled to a distributedstorage and/or task processing network (DSTN) module, or multiple DSTNmodules, via a network 24. The DST client module 34 includes an outboundDST processing section 80 and an inbound DST processing section 82. TheDSTN module includes a plurality of DST execution units. Each DSTexecution unit includes a controller 86, memory 88, one or moredistributed task (DT) execution modules 90, and a DST client module 34.

In an example of data storage, the DST client module 34 has data 92 thatit desires to store in the DSTN module. The data 92 may be a file (e.g.,video, audio, text, graphics, etc.), a data object, a data block, anupdate to a file, an update to a data block, etc. In this instance, theoutbound DST processing module 80 converts the data 92 into encoded dataslices 216 as will be further described with reference to FIGS. 21-23.The outbound DST processing module 80 sends, via the network 24, to theDST execution units for storage as further described with reference toFIG. 24.

In an example of data retrieval, the DST client module 34 issues aretrieve request to the DST execution units for the desired data 92. Theretrieve request may address each DST executions units storing encodeddata slices of the desired data, address a decode threshold number ofDST execution units, address a read threshold number of DST executionunits, or address some other number of DST execution units. In responseto the request, each addressed DST execution unit retrieves its encodeddata slices 100 of the desired data and sends them to the inbound DSTprocessing section 82, via the network 24.

When, for each data segment, the inbound DST processing section 82receives at least a decode threshold number of encoded data slices 100,it converts the encoded data slices 100 into a data segment. The inboundDST processing section 82 aggregates the data segments to produce theretrieved data 92.

FIG. 21 is a schematic block diagram of an embodiment of an outbounddistributed storage and/or task (DST) processing section 80 of a DSTclient module coupled to a distributed storage and task network (DSTN)module (e.g., a plurality of DST execution units) via a network 24. Theoutbound DST processing section 80 includes a data partitioning module110, a dispersed storage (DS) error encoding module 112, groupingselector module 114, a control module 116, and a distributed taskcontrol module 118.

In an example of operation, the data partitioning module 110 isby-passed such that data 92 is provided directly to the DS errorencoding module 112. The control module 116 coordinates the by-passingof the data partitioning module 110 by outputting a bypass 220 messageto the data partitioning module 110.

The DS error encoding module 112 receives the data 92 in a serialmanner, a parallel manner, and/or a combination thereof. The DS errorencoding module 112 DS error encodes the data in accordance with controlinformation 160 from the control module 116 to produce encoded dataslices 218. The DS error encoding includes segmenting the data 92 intodata segments, segment security processing (e.g., encryption,compression, watermarking, integrity check (e.g., CRC), etc.), errorencoding, slicing, and/or per slice security processing (e.g.,encryption, compression, watermarking, integrity check (e.g., CRC),etc.). The control information 160 indicates which steps of the DS errorencoding are active for the data 92 and, for active steps, indicates theparameters for the step. For example, the control information 160indicates that the error encoding is active and includes error encodingparameters (e.g., pillar width, decode threshold, write threshold, readthreshold, type of error encoding, etc.).

The grouping selector module 114 groups the encoded slices 218 of thedata segments into pillars of slices 216. The number of pillarscorresponds to the pillar width of the DS error encoding parameters. Inthis example, the distributed task control module 118 facilitates thestorage request.

FIG. 22 is a schematic block diagram of an example of a dispersedstorage (DS) error encoding module 112 for the example of FIG. 21. TheDS error encoding module 112 includes a segment processing module 142, asegment security processing module 144, an error encoding module 146, aslicing module 148, and a per slice security processing module 150. Eachof these modules is coupled to a control module 116 to receive controlinformation 160 therefrom.

In an example of operation, the segment processing module 142 receivesdata 92 and receives segmenting information as control information 160from the control module 116. The segmenting information indicates howthe segment processing module is to segment the data. For example, thesegmenting information indicates the size of each data segment. Thesegment processing module 142 segments the data 92 into data segments152 in accordance with the segmenting information.

The segment security processing module 144, when enabled by the controlmodule 116, secures the data segments 152 based on segment securityinformation received as control information 160 from the control module116. The segment security information includes data compression,encryption, watermarking, integrity check (e.g., CRC), etc., and/or anyother type of digital security. For example, when the segment securityprocessing module 144 is enabled, it compresses a data segment 152,encrypts the compressed data segment, and generates a CRC value for theencrypted data segment to produce a secure data segment. When thesegment security processing module 144 is not enabled, it passes thedata segments 152 to the error encoding module 146 or is bypassed suchthat the data segments 152 are provided to the error encoding module146.

The error encoding module 146 encodes the secure data segments inaccordance with error correction encoding parameters received as controlinformation 160 from the control module 116. The error correctionencoding parameters include identifying an error correction encodingscheme (e.g., forward error correction algorithm, a Reed-Solomon basedalgorithm, an information dispersal algorithm, etc.), a pillar width, adecode threshold, a read threshold, a write threshold, etc. For example,the error correction encoding parameters identify a specific errorcorrection encoding scheme, specifies a pillar width of five, andspecifies a decode threshold of three. From these parameters, the errorencoding module 146 encodes a data segment to produce an encoded datasegment.

The slicing module 148 slices the encoded data segment in accordancewith a pillar width of the error correction encoding parameters. Forexample, if the pillar width is five, the slicing module slices anencoded data segment into a set of five encoded data slices. As such,for a plurality of data segments, the slicing module 148 outputs aplurality of sets of encoded data slices as shown within encoding andslicing function 222 as described.

The per slice security processing module 150, when enabled by thecontrol module 116, secures each encoded data slice based on slicesecurity information received as control information 160 from thecontrol module 116. The slice security information includes datacompression, encryption, watermarking, integrity check (e.g., CRC),etc., and/or any other type of digital security. For example, when theper slice security processing module 150 is enabled, it may compress anencoded data slice, encrypt the compressed encoded data slice, andgenerate a CRC value for the encrypted encoded data slice to produce asecure encoded data slice tweaking. When the per slice securityprocessing module 150 is not enabled, it passes the encoded data slicesor is bypassed such that the encoded data slices 218 are the output ofthe DS error encoding module 112.

FIG. 23 is a diagram of an example of converting data 92 into pillarslice groups utilizing encoding, slicing and pillar grouping function224 for storage in memory of a distributed storage and task network(DSTN) module. As previously discussed the data 92 is encoded and slicedinto a plurality of sets of encoded data slices; one set per datasegment. The grouping selector module organizes the sets of encoded dataslices into pillars of data slices. In this example, the DS errorencoding parameters include a pillar width of 5 and a decode thresholdof 3. As such, for each data segment, 5 encoded data slices are created.

The grouping selector module takes the first encoded data slice of eachof the sets and forms a first pillar, which may be sent to the first DSTexecution unit. Similarly, the grouping selector module creates thesecond pillar from the second slices of the sets; the third pillar fromthe third slices of the sets; the fourth pillar from the fourth slicesof the sets; and the fifth pillar from the fifth slices of the set.

FIG. 24 is a schematic block diagram of an embodiment of a distributedstorage and/or task (DST) execution unit that includes an interface 169,a controller 86, memory 88, one or more distributed task (DT) executionmodules 90, and a DST client module 34. A computing core 26 may beutilized to implement the one or more DT execution modules 90 and theDST client module 34. The memory 88 is of sufficient size to store asignificant number of encoded data slices (e.g., thousands of slices tohundreds-of-millions of slices) and may include one or more hard drivesand/or one or more solid-state memory devices (e.g., flash memory, DRAM,etc.).

In an example of storing a pillar of slices 216, the DST execution unitreceives, via interface 169, a pillar of slices 216 (e.g., pillar #1slices). The memory 88 stores the encoded data slices 216 of the pillarof slices in accordance with memory control information 174 it receivesfrom the controller 86. The controller 86 (e.g., a processing module, aCPU, etc.) generates the memory control information 174 based ondistributed storage information (e.g., user information (e.g., user ID,distributed storage permissions, data access permission, etc.), vaultinformation (e.g., virtual memory assigned to user, user group, etc.),etc.). Similarly, when retrieving slices, the DST execution unitreceives, via interface 169, a slice retrieval request. The memory 88retrieves the slice in accordance with memory control information 174 itreceives from the controller 86. The memory 88 outputs the slice 100,via the interface 169, to a requesting entity.

FIG. 25 is a schematic block diagram of an example of operation of aninbound distributed storage and/or task (DST) processing section 82 forretrieving dispersed error encoded data 92. The inbound DST processingsection 82 includes a de-grouping module 180, a dispersed storage (DS)error decoding module 182, a data de-partitioning module 184, a controlmodule 186, and a distributed task control module 188. Note that thecontrol module 186 and/or the distributed task control module 188 may beseparate modules from corresponding ones of an outbound DST processingsection or may be the same modules.

In an example of operation, the inbound DST processing section 82 isretrieving stored data 92 from the DST execution units (i.e., the DSTNmodule). In this example, the DST execution units output encoded dataslices corresponding to data retrieval requests from the distributedtask control module 188. The de-grouping module 180 receives pillars ofslices 100 and de-groups them in accordance with control information 190from the control module 186 to produce sets of encoded data slices 218.The DS error decoding module 182 decodes, in accordance with the DSerror encoding parameters received as control information 190 from thecontrol module 186, each set of encoded data slices 218 to produce datasegments, which are aggregated into retrieved data 92. The datade-partitioning module 184 is by-passed in this operational mode via abypass signal 226 of control information 190 from the control module186.

FIG. 26 is a schematic block diagram of an embodiment of a dispersedstorage (DS) error decoding module 182 of an inbound distributed storageand task (DST) processing section. The DS error decoding module 182includes an inverse per slice security processing module 202, ade-slicing module 204, an error decoding module 206, an inverse segmentsecurity module 208, a de-segmenting processing module 210, and acontrol module 186. The dispersed error decoding module 182 is operableto de-slice and decode encoded slices per data segment 218 utilizing ade-slicing and decoding function 228 to produce a plurality of datasegments that are de-segmented utilizing a de-segment function 230 torecover data 92.

In an example of operation, the inverse per slice security processingmodule 202, when enabled by the control module 186 via controlinformation 190, unsecures each encoded data slice 218 based on slicede-security information (e.g., the compliment of the slice securityinformation discussed with reference to FIG. 6) received as controlinformation 190 from the control module 186. The slice de-securityinformation includes data decompression, decryption, de-watermarking,integrity check (e.g., CRC) verification, etc., and/or any other type ofdigital security. For example, when the inverse per slice securityprocessing module 202 is enabled, it verifies integrity information(e.g., a CRC value) of each encoded data slice 218, it decrypts eachverified encoded data slice, and decompresses each decrypted encodeddata slice to produce slice encoded data. When the inverse per slicesecurity processing module 202 is not enabled, it passes the encodeddata slices 218 as the sliced encoded data or is bypassed such that theretrieved encoded data slices 218 are provided as the sliced encodeddata.

The de-slicing module 204 de-slices the sliced encoded data into encodeddata segments in accordance with a pillar width of the error correctionencoding parameters received as control information 190 from the controlmodule 186. For example, if the pillar width is five, the de-slicingmodule de-slices a set of five encoded data slices into an encoded datasegment. Alternatively, the encoded data segment may include just threeencoded data slices (e.g., when the decode threshold is 3).

The error decoding module 206 decodes the encoded data segments inaccordance with error correction decoding parameters received as controlinformation 190 from the control module 186 to produce secure datasegments. The error correction decoding parameters include identifyingan error correction encoding scheme (e.g., forward error correctionalgorithm, a Reed-Solomon based algorithm, an information dispersalalgorithm, etc.), a pillar width, a decode threshold, a read threshold,a write threshold, etc. For example, the error correction decodingparameters identify a specific error correction encoding scheme, specifya pillar width of five, and specify a decode threshold of three.

The inverse segment security processing module 208, when enabled by thecontrol module 186, unsecures the secured data segments based on segmentsecurity information received as control information 190 from thecontrol module 186. The segment security information includes datadecompression, decryption, de-watermarking, integrity check (e.g., CRC),etc., verification, and/or any other type of digital security. Forexample, when the inverse segment security processing module is enabled,it verifies integrity information (e.g., a CRC value) of each securedata segment, it decrypts each verified secured data segment, anddecompresses each decrypted secure data segment to produce a datasegment 152. When the inverse segment security processing module 208 isnot enabled, it passes the decoded data segment 152 as the data segmentor is bypassed. The de-segmenting processing module 210 aggregates thedata segments 152 into the data 92 in accordance with controlinformation 190 from the control module 186.

FIG. 27 is a schematic block diagram of an example of a distributedstorage and task processing network (DSTN) module that includes aplurality of distributed storage and task (DST) execution units (#1through #n, where, for example, n is an integer greater than or equal tothree). Each of the DST execution units includes a DST client module 34,a controller 86, one or more DT (distributed task) execution modules 90,and memory 88.

In this example, the DSTN module stores, in the memory of the DSTexecution units, a plurality of DS (dispersed storage) encoded data(e.g., 1 through n, where n is an integer greater than or equal to two)and stores a plurality of DS encoded task codes (e.g., 1 through k,where k is an integer greater than or equal to two). The DS encoded datamay be encoded in accordance with one or more examples described withreference to FIGS. 3-19 (e.g., organized in slice groupings) or encodedin accordance with one or more examples described with reference toFIGS. 20-26 (e.g., organized in pillar groups). The data that is encodedinto the DS encoded data may be of any size and/or of any content. Forexample, the data may be one or more digital books, a copy of acompany's emails, a large-scale Internet search, a video security file,one or more entertainment video files (e.g., television programs,movies, etc.), data files, and/or any other large amount of data (e.g.,greater than a few Terra-Bytes).

The tasks that are encoded into the DS encoded task code may be a simplefunction (e.g., a mathematical function, a logic function, an identifyfunction, a find function, a search engine function, a replace function,etc.), a complex function (e.g., compression, human and/or computerlanguage translation, text-to-voice conversion, voice-to-textconversion, etc.), multiple simple and/or complex functions, one or morealgorithms, one or more applications, etc. The tasks may be encoded intothe DS encoded task code in accordance with one or more examplesdescribed with reference to FIGS. 3-19 (e.g., organized in slicegroupings) or encoded in accordance with one or more examples describedwith reference to FIGS. 20-26 (e.g., organized in pillar groups).

In an example of operation, a DST client module of a user device or of aDST processing unit issues a DST request to the DSTN module. The DSTrequest may include a request to retrieve stored data, or a portionthereof, may include a request to store data that is included with theDST request, may include a request to perform one or more tasks onstored data, may include a request to perform one or more tasks on dataincluded with the DST request, etc. In the cases where the DST requestincludes a request to store data or to retrieve data, the client moduleand/or the DSTN module processes the request as previously discussedwith reference to one or more of FIGS. 3-19 (e.g., slice groupings)and/or 20-26 (e.g., pillar groupings). In the case where the DST requestincludes a request to perform one or more tasks on data included withthe DST request, the DST client module and/or the DSTN module processthe DST request as previously discussed with reference to one or more ofFIGS. 3-19.

In the case where the DST request includes a request to perform one ormore tasks on stored data, the DST client module and/or the DSTN moduleprocesses the DST request as will be described with reference to one ormore of FIGS. 28-39. In general, the DST client module identifies dataand one or more tasks for the DSTN module to execute upon the identifieddata. The DST request may be for a one-time execution of the task or foran on-going execution of the task. As an example of the latter, as acompany generates daily emails, the DST request may be to daily searchnew emails for inappropriate content and, if found, record the content,the email sender(s), the email recipient(s), email routing information,notify human resources of the identified email, etc.

FIG. 28 is a schematic block diagram of an example of a distributedcomputing system performing tasks on stored data. In this example, twodistributed storage and task (DST) client modules 1-2 are shown: thefirst may be associated with a user device and the second may beassociated with a DST processing unit or a high priority user device(e.g., high priority clearance user, system administrator, etc.). EachDST client module includes a list of stored data 234 and a list of taskscodes 236. The list of stored data 234 includes one or more entries ofdata identifying information, where each entry identifies data stored inthe DSTN module 22. The data identifying information (e.g., data ID)includes one or more of a data file name, a data file directory listing,DSTN addressing information of the data, a data object identifier, etc.The list of tasks 236 includes one or more entries of task codeidentifying information, when each entry identifies task codes stored inthe DSTN module 22. The task code identifying information (e.g., taskID) includes one or more of a task file name, a task file directorylisting, DSTN addressing information of the task, another type ofidentifier to identify the task, etc.

As shown, the list of data 234 and the list of tasks 236 are eachsmaller in number of entries for the first DST client module than thecorresponding lists of the second DST client module. This may occurbecause the user device associated with the first DST client module hasfewer privileges in the distributed computing system than the deviceassociated with the second DST client module. Alternatively, this mayoccur because the user device associated with the first DST clientmodule serves fewer users than the device associated with the second DSTclient module and is restricted by the distributed computing systemaccordingly. As yet another alternative, this may occur through norestraints by the distributed computing system, it just occurred becausethe operator of the user device associated with the first DST clientmodule has selected fewer data and/or fewer tasks than the operator ofthe device associated with the second DST client module.

In an example of operation, the first DST client module selects one ormore data entries 238 and one or more tasks 240 from its respectivelists (e.g., selected data ID and selected task ID). The first DSTclient module sends its selections to a task distribution module 232.The task distribution module 232 may be within a stand-alone device ofthe distributed computing system, may be within the user device thatcontains the first DST client module, or may be within the DSTN module22.

Regardless of the task distribution module's location, it generates DSTallocation information 242 from the selected task ID 240 and theselected data ID 238. The DST allocation information 242 includes datapartitioning information, task execution information, and/orintermediate result information. The task distribution module 232 sendsthe DST allocation information 242 to the DSTN module 22. Note that oneor more examples of the DST allocation information will be discussedwith reference to one or more of FIGS. 29-39.

The DSTN module 22 interprets the DST allocation information 242 toidentify the stored DS encoded data (e.g., DS error encoded data 2) andto identify the stored DS error encoded task code (e.g., DS errorencoded task code 1). In addition, the DSTN module 22 interprets the DSTallocation information 242 to determine how the data is to bepartitioned and how the task is to be partitioned. The DSTN module 22also determines whether the selected DS error encoded data 238 needs tobe converted from pillar grouping to slice grouping. If so, the DSTNmodule 22 converts the selected DS error encoded data into slicegroupings and stores the slice grouping DS error encoded data byoverwriting the pillar grouping DS error encoded data or by storing itin a different location in the memory of the DSTN module 22 (i.e., doesnot overwrite the pillar grouping DS encoded data).

The DSTN module 22 partitions the data and the task as indicated in theDST allocation information 242 and sends the portions to selected DSTexecution units of the DSTN module 22. Each of the selected DSTexecution units performs its partial task(s) on its slice groupings toproduce partial results. The DSTN module 22 collects the partial resultsfrom the selected DST execution units and provides them, as resultinformation 244, to the task distribution module. The result information244 may be the collected partial results, one or more final results asproduced by the DSTN module 22 from processing the partial results inaccordance with the DST allocation information 242, or one or moreintermediate results as produced by the DSTN module 22 from processingthe partial results in accordance with the DST allocation information242.

The task distribution module 232 receives the result information 244 andprovides one or more final results 104 therefrom to the first DST clientmodule. The final result(s) 104 may be result information 244 or aresult(s) of the task distribution module's processing of the resultinformation 244.

In concurrence with processing the selected task of the first DST clientmodule, the distributed computing system may process the selectedtask(s) of the second DST client module on the selected data(s) of thesecond DST client module. Alternatively, the distributed computingsystem may process the second DST client module's request subsequent to,or preceding, that of the first DST client module. Regardless of theordering and/or parallel processing of the DST client module requests,the second DST client module provides its selected data 238 and selectedtask 240 to a task distribution module 232. If the task distributionmodule 232 is a separate device of the distributed computing system orwithin the DSTN module, the task distribution modules 232 coupled to thefirst and second DST client modules may be the same module. The taskdistribution module 232 processes the request of the second DST clientmodule in a similar manner as it processed the request of the first DSTclient module.

FIG. 29 is a schematic block diagram of an embodiment of a taskdistribution module 232 facilitating the example of FIG. 28. The taskdistribution module 232 includes a plurality of tables it uses togenerate distributed storage and task (DST) allocation information 242for selected data and selected tasks received from a DST client module.The tables include data storage information 248, task storageinformation 250, distributed task (DT) execution module information 252,and task

sub-task mapping information 246.

The data storage information table 248 includes a data identification(ID) field 260, a data size field 262, an addressing information field264, distributed storage (DS) information 266, and may further includeother information regarding the data, how it is stored, and/or how itcan be processed. For example, DS encoded data #1 has a data ID of 1, adata size of AA (e.g., a byte size of a few terra-bytes or more),addressing information of Addr_1_AA, and DS parameters of ⅗; SEG_1; andSLC_1. In this example, the addressing information may be a virtualaddress corresponding to the virtual address of the first storage word(e.g., one or more bytes) of the data and information on how tocalculate the other addresses, may be a range of virtual addresses forthe storage words of the data, physical addresses of the first storageword or the storage words of the data, may be a list of slices names ofthe encoded data slices of the data, etc. The DS parameters may includeidentity of an error encoding scheme, decode threshold/pillar width(e.g., ⅗ for the first data entry), segment security information (e.g.,SEG_1), per slice security information (e.g., SLC_1), and/or any otherinformation regarding how the data was encoded into data slices.

The task storage information table 250 includes a task identification(ID) field 268, a task size field 270, an addressing information field272, distributed storage (DS) information 274, and may further includeother information regarding the task, how it is stored, and/or how itcan be used to process data. For example, DS encoded task #2 has a taskID of 2, a task size of XY, addressing information of Addr_2_XY, and DSparameters of ⅗; SEG_2; and SLC_2. In this example, the addressinginformation may be a virtual address corresponding to the virtualaddress of the first storage word (e.g., one or more bytes) of the taskand information on how to calculate the other addresses, may be a rangeof virtual addresses for the storage words of the task, physicaladdresses of the first storage word or the storage words of the task,may be a list of slices names of the encoded slices of the task code,etc. The DS parameters may include identity of an error encoding scheme,decode threshold/pillar width (e.g., ⅗ for the first data entry),segment security information (e.g., SEG_2), per slice securityinformation (e.g., SLC_2), and/or any other information regarding howthe task was encoded into encoded task slices. Note that the segmentand/or the per-slice security information include a type of encryption(if enabled), a type of compression (if enabled), watermarkinginformation (if enabled), and/or an integrity check scheme (if enabled).

The task

sub-task mapping information table 246 includes a task field 256 and asub-task field 258. The task field 256 identifies a task stored in thememory of a distributed storage and task network (DSTN) module and thecorresponding sub-task fields 258 indicates whether the task includessub-tasks and, if so, how many and if any of the sub-tasks are ordered.In this example, the task

sub-task mapping information table 246 includes an entry for each taskstored in memory of the DSTN module (e.g., task 1 through task k). Inparticular, this example indicates that task 1 includes 7 sub-tasks;task 2 does not include sub-tasks, and task k includes r number ofsub-tasks (where r is an integer greater than or equal to two).

The DT execution module table 252 includes a DST execution unit ID field276, a DT execution module ID field 278, and a DT execution modulecapabilities field 280. The DST execution unit ID field 276 includes theidentity of DST units in the DSTN module. The DT execution module IDfield 278 includes the identity of each DT execution unit in each DSTunit. For example, DST unit 1 includes three DT executions modules(e.g., 1_1, 1_2, and 1_3). The DT execution capabilities field 280includes identity of the capabilities of the corresponding DT executionunit. For example, DT execution module 1_1 includes capabilities X,where X includes one or more of MIPS capabilities, processing resources(e.g., quantity and capability of microprocessors, CPUs, digital signalprocessors, co-processor, microcontrollers, arithmetic logic circuitry,and/or any other analog and/or digital processing circuitry),availability of the processing resources, memory information (e.g.,type, size, availability, etc.), and/or any information germane toexecuting one or more tasks.

From these tables, the task distribution module 232 generates the DSTallocation information 242 to indicate where the data is stored, how topartition the data, where the task is stored, how to partition the task,which DT execution units should perform which partial task on which datapartitions, where and how intermediate results are to be stored, etc. Ifmultiple tasks are being performed on the same data or different data,the task distribution module factors such information into itsgeneration of the DST allocation information.

FIG. 30 is a diagram of a specific example of a distributed computingsystem performing tasks on stored data as a task flow 318. In thisexample, selected data 92 is data 2 and selected tasks are tasks 1, 2,and 3. Task 1 corresponds to analyzing translation of data from onelanguage to another (e.g., human language or computer language); task 2corresponds to finding specific words and/or phrases in the data; andtask 3 corresponds to finding specific translated words and/or phrasesin translated data.

In this example, task 1 includes 7 sub-tasks: task 1_1—identifynon-words (non-ordered); task 1_2—identify unique words (non-ordered);task 1_3—translate (non-ordered); task 1_4—translate back (ordered aftertask 1_3); task 1_5—compare to ID errors (ordered after task 1-4); task1_6—determine non-word translation errors (ordered after task 1_5 and1_1); and task 1_7—determine correct translations (ordered after 1_5 and1_2). The sub-task further indicates whether they are an ordered task(i.e., are dependent on the outcome of another task) or non-order (i.e.,are independent of the outcome of another task). Task 2 does not includesub-tasks and task 3 includes two sub-tasks: task 3_1 translate; andtask 3_2 find specific word or phrase in translated data.

In general, the three tasks collectively are selected to analyze datafor translation accuracies, translation errors, translation anomalies,occurrence of specific words or phrases in the data, and occurrence ofspecific words or phrases on the translated data. Graphically, the data92 is translated 306 into translated data 282; is analyzed for specificwords and/or phrases 300 to produce a list of specific words and/orphrases 286; is analyzed for non-words 302 (e.g., not in a referencedictionary) to produce a list of non-words 290; and is analyzed forunique words 316 included in the data 92 (i.e., how many different wordsare included in the data) to produce a list of unique words 298. Each ofthese tasks is independent of each other and can therefore be processedin parallel if desired.

The translated data 282 is analyzed (e.g., sub-task 3_2) for specifictranslated words and/or phrases 304 to produce a list of specifictranslated words and/or phrases 288. The translated data 282 istranslated back 308 (e.g., sub-task 1_4) into the language of theoriginal data to produce re-translated data 284. These two tasks aredependent on the translate task (e.g., task 1_3) and thus must beordered after the translation task, which may be in a pipelined orderingor a serial ordering. The re-translated data 284 is then compared 310with the original data 92 to find words and/or phrases that did nottranslate (one way and/or the other) properly to produce a list ofincorrectly translated words 294. As such, the comparing task (e.g.,sub-task 1_5) 310 is ordered after the translation 306 andre-translation tasks 308 (e.g., sub-tasks 1_3 and 1_4).

The list of words incorrectly translated 294 is compared 312 to the listof non-words 290 to identify words that were not properly translatedbecause the words are non-words to produce a list of errors due tonon-words 292. In addition, the list of words incorrectly translated 294is compared 314 to the list of unique words 298 to identify unique wordsthat were properly translated to produce a list of correctly translatedwords 296. The comparison may also identify unique words that were notproperly translated to produce a list of unique words that were notproperly translated. Note that each list of words (e.g., specific wordsand/or phrases, non-words, unique words, translated words and/orphrases, etc.) may include the word and/or phrase, how many times it isused, where in the data it is used, and/or any other informationrequested regarding a word and/or phrase.

FIG. 31 is a schematic block diagram of an example of a distributedstorage and task processing network (DSTN) module storing data and taskcodes for the example of FIG. 30. As shown, DS encoded data 2 is storedas encoded data slices across the memory (e.g., stored in memories 88)of DST execution units 1-5; the DS encoded task code 1 (of task 1) andDS encoded task 3 are stored as encoded task slices across the memory ofDST execution units 1-5; and DS encoded task code 2 (of task 2) isstored as encoded task slices across the memory of DST execution units3-7. As indicated in the data storage information table and the taskstorage information table of FIG. 29, the respective data/task has DSparameters of ⅗ for their decode threshold/pillar width; hence spanningthe memory of five DST execution units.

FIG. 32 is a diagram of an example of distributed storage and task (DST)allocation information 242 for the example of FIG. 30. The DSTallocation information 242 includes data partitioning information 320,task execution information 322, and intermediate result information 324.The data partitioning information 320 includes the data identifier (ID),the number of partitions to split the data into, address information foreach data partition, and whether the DS encoded data has to betransformed from pillar grouping to slice grouping. The task executioninformation 322 includes tabular information having a taskidentification field 326, a task ordering field 328, a data partitionfield ID 330, and a set of DT execution modules 332 to use for thedistributed task processing per data partition. The intermediate resultinformation 324 includes tabular information having a name ID field 334,an ID of the DST execution unit assigned to process the correspondingintermediate result 336, a scratch pad storage field 338, and anintermediate result storage field 340.

Continuing with the example of FIG. 30, where tasks 1-3 are to bedistributedly performed on data 2, the data partitioning informationincludes the ID of data 2. In addition, the task distribution moduledetermines whether the DS encoded data 2 is in the proper format fordistributed computing (e.g., was stored as slice groupings). If not, thetask distribution module indicates that the DS encoded data 2 formatneeds to be changed from the pillar grouping format to the slicegrouping format, which will be done by the DSTN module. In addition, thetask distribution module determines the number of partitions to dividethe data into (e.g., 2_1 through 2_z) and addressing information foreach partition.

The task distribution module generates an entry in the task executioninformation section for each sub-task to be performed. For example, task1_1 (e.g., identify non-words on the data) has no task ordering (i.e.,is independent of the results of other sub-tasks), is to be performed ondata partitions 2_1 through 2_z by DT execution modules 1_1, 2_1, 3_1,4_1, and 5_1. For instance, DT execution modules 1_1, 2_1, 3_1, 4_1, and5_1 search for non-words in data partitions 2_1 through 2_z to producetask 1_1 intermediate results (R1-1, which is a list of non-words). Task1_2 (e.g., identify unique words) has similar task execution informationas task 1_1 to produce task 1_2 intermediate results (R1-2, which is thelist of unique words).

Task 1_3 (e.g., translate) includes task execution information as beingnon-ordered (i.e., is independent), having DT execution modules 1_1,2_1, 3_1, 4_1, and 5_1 translate data partitions 2_1 through 2_4 andhaving DT execution modules 1_2, 2_2, 3_2, 4_2, and 5_2 translate datapartitions 2_5 through 2_z to produce task 1_3 intermediate results(R1-3, which is the translated data). In this example, the datapartitions are grouped, where different sets of DT execution modulesperform a distributed sub-task (or task) on each data partition group,which allows for further parallel processing.

Task 1_4 (e.g., translate back) is ordered after task 1_3 and is to beexecuted on task 1_3's intermediate result (e.g., R1-3_1) (e.g., thetranslated data). DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1 areallocated to translate back task 1_3 intermediate result partitionsR1-3_1 through R1-3_4 and DT execution modules 1_2, 2_2, 6_1, 7_1, and7_2 are allocated to translate back task 1_3 intermediate resultpartitions R1-3_5 through R1-3_z to produce task 1-4 intermediateresults (R1-4, which is the translated back data).

Task 1_5 (e.g., compare data and translated data to identify translationerrors) is ordered after task 1_4 and is to be executed on task 1_4'sintermediate results (R4-1) and on the data. DT execution modules 1_1,2_1, 3_1, 4_1, and 5_1 are allocated to compare the data partitions (2_1through 2_z) with partitions of task 1-4 intermediate results partitionsR1-4_1 through R1-4_z to produce task 1_5 intermediate results (R1-5,which is the list words translated incorrectly).

Task 1_6 (e.g., determine non-word translation errors) is ordered aftertasks 1_1 and 1_5 and is to be executed on tasks 1_1's and 1_5'sintermediate results (R1-1 and R1-5). DT execution modules 1_1, 2_1,3_1, 4_1, and 5_1 are allocated to compare the partitions of task 1_1intermediate results (R1-1_1 through R1-1_z) with partitions of task 1-5intermediate results partitions (R1-5_1 through R1-5_z) to produce task1_6 intermediate results (R1-6, which is the list translation errors dueto non-words).

Task 1_7 (e.g., determine words correctly translated) is ordered aftertasks 1_2 and 1_5 and is to be executed on tasks 1_2's and 1_5'sintermediate results (R1-1 and R1-5). DT execution modules 1_2, 2_2,3_2, 4_2, and 5_2 are allocated to compare the partitions of task 1_2intermediate results (R1-2_1 through R1-2_z) with partitions of task 1-5intermediate results partitions (R1-5_1 through R1-5_z) to produce task1_7 intermediate results (R1-7, which is the list of correctlytranslated words).

Task 2 (e.g., find specific words and/or phrases) has no task ordering(i.e., is independent of the results of other sub-tasks), is to beperformed on data partitions 2_1 through 2_z by DT execution modules3_1, 4_1, 5_1, 6_1, and 7_1. For instance, DT execution modules 3_1,4_1, 5_1, 6_1, and 7_1 search for specific words and/or phrases in datapartitions 2_1 through 2_z to produce task 2 intermediate results (R2,which is a list of specific words and/or phrases).

Task 3_2 (e.g., find specific translated words and/or phrases) isordered after task 1_3 (e.g., translate) is to be performed onpartitions R1-3_1 through R1-3_z by DT execution modules 1_2, 2_2, 3_2,4_2, and 5_2. For instance, DT execution modules 1_2, 2_2, 3_2, 4_2, and5_2 search for specific translated words and/or phrases in thepartitions of the translated data (R1-3_1 through R1-3_z) to producetask 3_2 intermediate results (R3-2, which is a list of specifictranslated words and/or phrases).

For each task, the intermediate result information indicates which DSTunit is responsible for overseeing execution of the task and, if needed,processing the partial results generated by the set of allocated DTexecution units. In addition, the intermediate result informationindicates a scratch pad memory for the task and where the correspondingintermediate results are to be stored. For example, for intermediateresult R1-1 (the intermediate result of task 1_1), DST unit 1 isresponsible for overseeing execution of the task 1_1 and coordinatesstorage of the intermediate result as encoded intermediate result slicesstored in memory of DST execution units 1-5. In general, the scratch padis for storing non-DS encoded intermediate results and the intermediateresult storage is for storing DS encoded intermediate results.

FIGS. 33-38 are schematic block diagrams of the distributed storage andtask network (DSTN) module performing the example of FIG. 30. In FIG.33, the DSTN module accesses the data 92 and partitions it into aplurality of partitions 1-z in accordance with distributed storage andtask network (DST) allocation information. For each data partition, theDSTN identifies a set of its DT (distributed task) execution modules 90to perform the task (e.g., identify non-words (i.e., not in a referencedictionary) within the data partition) in accordance with the DSTallocation information. From data partition to data partition, the setof DT execution modules 90 may be the same, different, or a combinationthereof (e.g., some data partitions use the same set while other datapartitions use different sets).

For the first data partition, the first set of DT execution modules(e.g., 1_1, 2_1, 3_1, 4_1, and 5_1 per the DST allocation information ofFIG. 32) executes task 1_1 to produce a first partial result 102 ofnon-words found in the first data partition. The second set of DTexecution modules (e.g., 1_1, 2_1, 3_1, 4_1, and 5_1 per the DSTallocation information of FIG. 32) executes task 1_1 to produce a secondpartial result 102 of non-words found in the second data partition. Thesets of DT execution modules (as per the DST allocation information)perform task 1_1 on the data partitions until the “z” set of DTexecution modules performs task 1_1 on the “zth” data partition toproduce a “zth” partial result 102 of non-words found in the “zth” datapartition.

As indicated in the DST allocation information of FIG. 32, DST executionunit 1 is assigned to process the first through “zth” partial results toproduce the first intermediate result (R1-1), which is a list ofnon-words found in the data. For instance, each set of DT executionmodules 90 stores its respective partial result in the scratchpad memoryof DST execution unit 1 (which is identified in the DST allocation ormay be determined by DST execution unit 1). A processing module of DSTexecution 1 is engaged to aggregate the first through “zth” partialresults to produce the first intermediate result (e.g., R1_1). Theprocessing module stores the first intermediate result as non-DS errorencoded data in the scratchpad memory or in another section of memory ofDST execution unit 1.

DST execution unit 1 engages its DST client module to slice groupingbased DS error encode the first intermediate result (e.g., the list ofnon-words). To begin the encoding, the DST client module determineswhether the list of non-words is of a sufficient size to partition(e.g., greater than a Terra-Byte). If yes, it partitions the firstintermediate result (R1-1) into a plurality of partitions (e.g., R1-1_1through R1-1_m). If the first intermediate result is not of sufficientsize to partition, it is not partitioned.

For each partition of the first intermediate result, or for the firstintermediate result, the DST client module uses the DS error encodingparameters of the data (e.g., DS parameters of data 2, which includes ⅗decode threshold/pillar width ratio) to produce slice groupings. Theslice groupings are stored in the intermediate result memory (e.g.,allocated memory in the memories of DST execution units 1-5).

In FIG. 34, the DSTN module is performing task 1_2 (e.g., find uniquewords) on the data 92. To begin, the DSTN module accesses the data 92and partitions it into a plurality of partitions 1-z in accordance withthe DST allocation information or it may use the data partitions of task1_1 if the partitioning is the same. For each data partition, the DSTNidentifies a set of its DT execution modules to perform task 1_2 inaccordance with the DST allocation information. From data partition todata partition, the set of DT execution modules may be the same,different, or a combination thereof. For the data partitions, theallocated set of DT execution modules executes task 1_2 to produce apartial results (e.g., 1^(st) through “zth”) of unique words found inthe data partitions.

As indicated in the DST allocation information of FIG. 32, DST executionunit 1 is assigned to process the first through “zth” partial results102 of task 1_2 to produce the second intermediate result (R1-2), whichis a list of unique words found in the data 92. The processing module ofDST execution 1 is engaged to aggregate the first through “zth” partialresults of unique words to produce the second intermediate result. Theprocessing module stores the second intermediate result as non-DS errorencoded data in the scratchpad memory or in another section of memory ofDST execution unit 1.

DST execution unit 1 engages its DST client module to slice groupingbased DS error encode the second intermediate result (e.g., the list ofnon-words). To begin the encoding, the DST client module determineswhether the list of unique words is of a sufficient size to partition(e.g., greater than a Terra-Byte). If yes, it partitions the secondintermediate result (R1-2) into a plurality of partitions (e.g., R1-2_1through R1-2_m). If the second intermediate result is not of sufficientsize to partition, it is not partitioned.

For each partition of the second intermediate result, or for the secondintermediate results, the DST client module uses the DS error encodingparameters of the data (e.g., DS parameters of data 2, which includes ⅗decode threshold/pillar width ratio) to produce slice groupings. Theslice groupings are stored in the intermediate result memory (e.g.,allocated memory in the memories of DST execution units 1-5).

In FIG. 35, the DSTN module is performing task 1_3 (e.g., translate) onthe data 92. To begin, the DSTN module accesses the data 92 andpartitions it into a plurality of partitions 1-z in accordance with theDST allocation information or it may use the data partitions of task 1_1if the partitioning is the same. For each data partition, the DSTNidentifies a set of its DT execution modules to perform task 1_3 inaccordance with the DST allocation information (e.g., DT executionmodules 1_1, 2_1, 3_1, 4_1, and 5_1 translate data partitions 2_1through 2_4 and DT execution modules 1_2, 2_2, 3_2, 4_2, and 5_2translate data partitions 2_5 through 2_z). For the data partitions, theallocated set of DT execution modules 90 executes task 1_3 to producepartial results 102 (e.g., 1^(st) through “zth”) of translated data.

As indicated in the DST allocation information of FIG. 32, DST executionunit 2 is assigned to process the first through “zth” partial results oftask 1_3 to produce the third intermediate result (R1-3), which istranslated data. The processing module of DST execution 2 is engaged toaggregate the first through “zth” partial results of translated data toproduce the third intermediate result. The processing module stores thethird intermediate result as non-DS error encoded data in the scratchpadmemory or in another section of memory of DST execution unit 2.

DST execution unit 2 engages its DST client module to slice groupingbased DS error encode the third intermediate result (e.g., translateddata). To begin the encoding, the DST client module partitions the thirdintermediate result (R1-3) into a plurality of partitions (e.g., R1-3_1through R1-3_y). For each partition of the third intermediate result,the DST client module uses the DS error encoding parameters of the data(e.g., DS parameters of data 2, which includes ⅗ decode threshold/pillarwidth ratio) to produce slice groupings. The slice groupings are storedin the intermediate result memory (e.g., allocated memory in thememories of DST execution units 2-6 per the DST allocation information).

As is further shown in FIG. 35, the DSTN module is performing task 1_4(e.g., retranslate) on the translated data of the third intermediateresult. To begin, the DSTN module accesses the translated data (from thescratchpad memory or from the intermediate result memory and decodes it)and partitions it into a plurality of partitions in accordance with theDST allocation information. For each partition of the third intermediateresult, the DSTN identifies a set of its DT execution modules 90 toperform task 1_4 in accordance with the DST allocation information(e.g., DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1 are allocated totranslate back partitions R1-3_1 through R1-3_4 and DT execution modules1_2, 2_2, 6_1, 7_1, and 7_2 are allocated to translate back partitionsR1-3_5 through R1-3_z). For the partitions, the allocated set of DTexecution modules executes task 1_4 to produce partial results 102(e.g., 1^(st) through “zth”) of re-translated data.

As indicated in the DST allocation information of FIG. 32, DST executionunit 3 is assigned to process the first through “zth” partial results oftask 1_4 to produce the fourth intermediate result (R1-4), which isretranslated data. The processing module of DST execution 3 is engagedto aggregate the first through “zth” partial results of retranslateddata to produce the fourth intermediate result. The processing modulestores the fourth intermediate result as non-DS error encoded data inthe scratchpad memory or in another section of memory of DST executionunit 3.

DST execution unit 3 engages its DST client module to slice groupingbased DS error encode the fourth intermediate result (e.g., retranslateddata). To begin the encoding, the DST client module partitions thefourth intermediate result (R1-4) into a plurality of partitions (e.g.,R1-4_1 through R1-4_z). For each partition of the fourth intermediateresult, the DST client module uses the DS error encoding parameters ofthe data (e.g., DS parameters of data 2, which includes ⅗ decodethreshold/pillar width ratio) to produce slice groupings. The slicegroupings are stored in the intermediate result memory (e.g., allocatedmemory in the memories of DST execution units 3-7 per the DST allocationinformation).

In FIG. 36, a distributed storage and task network (DSTN) module isperforming task 1_5 (e.g., compare) on data 92 and retranslated data ofFIG. 35. To begin, the DSTN module accesses the data 92 and partitionsit into a plurality of partitions in accordance with the DST allocationinformation or it may use the data partitions of task 1_1 if thepartitioning is the same. The DSTN module also accesses the retranslateddata from the scratchpad memory, or from the intermediate result memoryand decodes it, and partitions it into a plurality of partitions inaccordance with the DST allocation information. The number of partitionsof the retranslated data corresponds to the number of partitions of thedata.

For each pair of partitions (e.g., data partition 1 and retranslateddata partition 1), the DSTN identifies a set of its DT execution modules90 to perform task 1_5 in accordance with the DST allocation information(e.g., DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1). For each pairof partitions, the allocated set of DT execution modules executes task1_5 to produce partial results 102 (e.g., 1^(st) through “zth”) of alist of incorrectly translated words and/or phrases.

As indicated in the DST allocation information of FIG. 32, DST executionunit 1 is assigned to process the first through “zth” partial results oftask 1_5 to produce the fifth intermediate result (R1-5), which is thelist of incorrectly translated words and/or phrases. In particular, theprocessing module of DST execution 1 is engaged to aggregate the firstthrough “zth” partial results of the list of incorrectly translatedwords and/or phrases to produce the fifth intermediate result. Theprocessing module stores the fifth intermediate result as non-DS errorencoded data in the scratchpad memory or in another section of memory ofDST execution unit 1.

DST execution unit 1 engages its DST client module to slice groupingbased DS error encode the fifth intermediate result. To begin theencoding, the DST client module partitions the fifth intermediate result(R1-5) into a plurality of partitions (e.g., R1-5_1 through R1-5_z). Foreach partition of the fifth intermediate result, the DST client moduleuses the DS error encoding parameters of the data (e.g., DS parametersof data 2, which includes ⅗ decode threshold/pillar width ratio) toproduce slice groupings. The slice groupings are stored in theintermediate result memory (e.g., allocated memory in the memories ofDST execution units 1-5 per the DST allocation information).

As is further shown in FIG. 36, the DSTN module is performing task 1_6(e.g., translation errors due to non-words) on the list of incorrectlytranslated words and/or phrases (e.g., the fifth intermediate resultR1-5) and the list of non-words (e.g., the first intermediate resultR1-1). To begin, the DSTN module accesses the lists and partitions theminto a corresponding number of partitions.

For each pair of partitions (e.g., partition R1-1_1 and partitionR1-5_1), the DSTN identifies a set of its DT execution modules 90 toperform task 1_6 in accordance with the DST allocation information(e.g., DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1). For each pairof partitions, the allocated set of DT execution modules executes task1_6 to produce partial results 102 (e.g., 1^(st) through “zth”) of alist of incorrectly translated words and/or phrases due to non-words.

As indicated in the DST allocation information of FIG. 32, DST executionunit 2 is assigned to process the first through “zth” partial results oftask 1_6 to produce the sixth intermediate result (R1-6), which is thelist of incorrectly translated words and/or phrases due to non-words. Inparticular, the processing module of DST execution 2 is engaged toaggregate the first through “zth” partial results of the list ofincorrectly translated words and/or phrases due to non-words to producethe sixth intermediate result. The processing module stores the sixthintermediate result as non-DS error encoded data in the scratchpadmemory or in another section of memory of DST execution unit 2.

DST execution unit 2 engages its DST client module to slice groupingbased DS error encode the sixth intermediate result. To begin theencoding, the DST client module partitions the sixth intermediate result(R1-6) into a plurality of partitions (e.g., R1-6_1 through R1-6_z). Foreach partition of the sixth intermediate result, the DST client moduleuses the DS error encoding parameters of the data (e.g., DS parametersof data 2, which includes ⅗ decode threshold/pillar width ratio) toproduce slice groupings. The slice groupings are stored in theintermediate result memory (e.g., allocated memory in the memories ofDST execution units 2-6 per the DST allocation information).

As is still further shown in FIG. 36, the DSTN module is performing task1_7 (e.g., correctly translated words and/or phrases) on the list ofincorrectly translated words and/or phrases (e.g., the fifthintermediate result R1-5) and the list of unique words (e.g., the secondintermediate result R1-2). To begin, the DSTN module accesses the listsand partitions them into a corresponding number of partitions.

For each pair of partitions (e.g., partition R1-2_1 and partitionR1-5_1), the DSTN identifies a set of its DT execution modules 90 toperform task 1_7 in accordance with the DST allocation information(e.g., DT execution modules 1_2, 2_2, 3_2, 4_2, and 5_2). For each pairof partitions, the allocated set of DT execution modules executes task1_7 to produce partial results 102 (e.g., 1^(st) through “zth”) of alist of correctly translated words and/or phrases.

As indicated in the DST allocation information of FIG. 32, DST executionunit 3 is assigned to process the first through “zth” partial results oftask 1_7 to produce the seventh intermediate result (R1-7), which is thelist of correctly translated words and/or phrases. In particular, theprocessing module of DST execution 3 is engaged to aggregate the firstthrough “zth” partial results of the list of correctly translated wordsand/or phrases to produce the seventh intermediate result. Theprocessing module stores the seventh intermediate result as non-DS errorencoded data in the scratchpad memory or in another section of memory ofDST execution unit 3.

DST execution unit 3 engages its DST client module to slice groupingbased DS error encode the seventh intermediate result. To begin theencoding, the DST client module partitions the seventh intermediateresult (R1-7) into a plurality of partitions (e.g., R1-7_1 throughR1-7_z). For each partition of the seventh intermediate result, the DSTclient module uses the DS error encoding parameters of the data (e.g.,DS parameters of data 2, which includes ⅗ decode threshold/pillar widthratio) to produce slice groupings. The slice groupings are stored in theintermediate result memory (e.g., allocated memory in the memories ofDST execution units 3-7 per the DST allocation information).

In FIG. 37, the distributed storage and task network (DSTN) module isperforming task 2 (e.g., find specific words and/or phrases) on the data92. To begin, the DSTN module accesses the data and partitions it into aplurality of partitions 1-z in accordance with the DST allocationinformation or it may use the data partitions of task 1_1 if thepartitioning is the same. For each data partition, the DSTN identifies aset of its DT execution modules 90 to perform task 2 in accordance withthe DST allocation information. From data partition to data partition,the set of DT execution modules may be the same, different, or acombination thereof. For the data partitions, the allocated set of DTexecution modules executes task 2 to produce partial results 102 (e.g.,1^(st) through “zth”) of specific words and/or phrases found in the datapartitions.

As indicated in the DST allocation information of FIG. 32, DST executionunit 7 is assigned to process the first through “zth” partial results oftask 2 to produce task 2 intermediate result (R2), which is a list ofspecific words and/or phrases found in the data. The processing moduleof DST execution 7 is engaged to aggregate the first through “zth”partial results of specific words and/or phrases to produce the task 2intermediate result. The processing module stores the task 2intermediate result as non-DS error encoded data in the scratchpadmemory or in another section of memory of DST execution unit 7.

DST execution unit 7 engages its DST client module to slice groupingbased DS error encode the task 2 intermediate result. To begin theencoding, the DST client module determines whether the list of specificwords and/or phrases is of a sufficient size to partition (e.g., greaterthan a Terra-Byte). If yes, it partitions the task 2 intermediate result(R2) into a plurality of partitions (e.g., R2_1 through R2_m). If thetask 2 intermediate result is not of sufficient size to partition, it isnot partitioned.

For each partition of the task 2 intermediate result, or for the task 2intermediate results, the DST client module uses the DS error encodingparameters of the data (e.g., DS parameters of data 2, which includes ⅗decode threshold/pillar width ratio) to produce slice groupings. Theslice groupings are stored in the intermediate result memory (e.g.,allocated memory in the memories of DST execution units 1-4, and 7).

In FIG. 38, the distributed storage and task network (DSTN) module isperforming task 3 (e.g., find specific translated words and/or phrases)on the translated data (R1-3). To begin, the DSTN module accesses thetranslated data (from the scratchpad memory or from the intermediateresult memory and decodes it) and partitions it into a plurality ofpartitions in accordance with the DST allocation information. For eachpartition, the DSTN identifies a set of its DT execution modules toperform task 3 in accordance with the DST allocation information. Frompartition to partition, the set of DT execution modules may be the same,different, or a combination thereof. For the partitions, the allocatedset of DT execution modules 90 executes task 3 to produce partialresults 102 (e.g., 1^(st) through “zth”) of specific translated wordsand/or phrases found in the data partitions.

As indicated in the DST allocation information of FIG. 32, DST executionunit 5 is assigned to process the first through “zth” partial results oftask 3 to produce task 3 intermediate result (R3), which is a list ofspecific translated words and/or phrases found in the translated data.In particular, the processing module of DST execution 5 is engaged toaggregate the first through “zth” partial results of specific translatedwords and/or phrases to produce the task 3 intermediate result. Theprocessing module stores the task 3 intermediate result as non-DS errorencoded data in the scratchpad memory or in another section of memory ofDST execution unit 7.

DST execution unit 5 engages its DST client module to slice groupingbased DS error encode the task 3 intermediate result. To begin theencoding, the DST client module determines whether the list of specifictranslated words and/or phrases is of a sufficient size to partition(e.g., greater than a Terra-Byte). If yes, it partitions the task 3intermediate result (R3) into a plurality of partitions (e.g., R3_1through R3_m). If the task 3 intermediate result is not of sufficientsize to partition, it is not partitioned.

For each partition of the task 3 intermediate result, or for the task 3intermediate results, the DST client module uses the DS error encodingparameters of the data (e.g., DS parameters of data 2, which includes ⅗decode threshold/pillar width ratio) to produce slice groupings. Theslice groupings are stored in the intermediate result memory (e.g.,allocated memory in the memories of DST execution units 1-4, 5, and 7).

FIG. 39 is a diagram of an example of combining result information intofinal results 104 for the example of FIG. 30. In this example, theresult information includes the list of specific words and/or phrasesfound in the data (task 2 intermediate result), the list of specifictranslated words and/or phrases found in the data (task 3 intermediateresult), the list of non-words found in the data (task 1 firstintermediate result R1-1), the list of unique words found in the data(task 1 second intermediate result R1-2), the list of translation errorsdue to non-words (task 1 sixth intermediate result R1-6), and the listof correctly translated words and/or phrases (task 1 seventhintermediate result R1-7). The task distribution module provides theresult information to the requesting DST client module as the results104.

FIG. 40A is a diagram of an example embodiment of a dispersed storageand task execution unit 36 that includes an interface 169, a computingcore 26, a controller 86, at least one memory 88, and one or more memorymodules 350. A memory module 350 of the one or more memory modules 350may include a memory device 352 (e.g., implemented utilizing FLASHmemory technology, a random access memory, a read-only memory, amagnetic disk drive, and an optical disk drive), may include one or moredistributed task (DT) execution modules 90 (e.g., implemented utilizingat least one of a processing module, and a computing core), and mayinclude one or more DST client module 34. For example, a memory device352 is implemented by adding a processing core (e.g., to enable a DTexecution module) to a FLASH memory. As another example, a memory device352 is implemented by adding four processing cores to the FLASH memory.Alternatively, or in addition to, the memory device 352 includes one ormore distributed storage and task (DST) client modules 34. As yetanother example, a memory module 350 is implemented as a disk drive unitthat includes one DT execution module 90 and four memory devices 352(e.g., disk drives). As a still further example, a memory module 350 isimplemented as a disk drive unit that includes 100 DT execution modules90 and 10 memory devices 352 (e.g., disk drives).

FIG. 40B is a flowchart illustrating an example of storing andprocessing a group of slices. The method begins at step 354 where aprocessing module (e.g., of a distributed task (DT) execution module ofa distributed storage and task execution (DST EX) unit embedded within adisk drive unit) receives at least one partial task with regards to agroup of slices of contiguous data (e.g., from a DST client module). Themethod continues at step 356 where the processing module receives slicesof the group of slices to produce received slices. The method continuesat step 358 where, when an interim threshold number (e.g., a maximumnumber of bytes limited by an ingestion cache memory) of received sliceshas been received, the processing module streams the received slices toa disk drive for storage therein. The streaming may provide a writebandwidth system improvement for the group of slices (e.g., as the groupof slices pertain to the contiguous data).

The method continues at step 360 where the processing module determineswhether to execute a partial task. The determining may be based on oneor more of comparing an amount of data received to a data threshold, apartial task type, task execution resource availability, and a taskschedule. For example, the processing module determines to execute thepartial task when data of the received slices can be processed inaccordance with a partial task. The method branches to step 364 when theprocessing module determines to execute the partial task. The methodcontinues to step 362 when the processing module determines not toexecute the partial task.

The method continues at step 362 where the processing module determineswhether more slices are expected. The determining may be based on one ormore of a contiguous data size indicator, a query, a lookup, and anumber of bytes received so far. The method repeats back to step 356when the processing module determines that there are more slices. Themethod continues to step 364 when the processing module determines thatthere are no more slices. The method continues at step 364 where theprocessing module determines execution steps and schedule. Thedetermining may be based on one or more of the at least one partialtask, the data, a previous task schedule, a schedule template, a taskexecution resource availability level, and a task execution requirement.The method continues at step 366 where the processing module identifiesa portion of the contiguous data for execution of one or more steps ofthe execution steps. The identifying includes matching the portion ofthe contiguous data to the one or more steps of execution steps based onone or more of a data type indicator associated with the portion, a datatype associated with one or more steps, and a data available indicator.

The method continues at step 368 where the processing module retrievesthe portion of the contiguous data from the disk drive as a data stream.The retrieving includes accessing the disk drive for multiple contiguousdata bytes. The streaming may provide a read bandwidth systemimprovement for the portion of data. The method continues at step 370where the processing module executes the steps in accordance with theschedule on the portion of the contiguous data to produce a partialresult. For example, the processing module executes a search partialtask on the portion to produce a search partial result.

The method continues at step 372 where the processing module dispersedstorage error encodes the partial results to produce a plurality of setsof slices in accordance with dispersal parameters associated with one ormore of the group of slices and the at least one partial task. Themethod continues at step 374 where the processing module facilitatesstoring a plurality of sets of slices in a dispersed storage and tasknetwork (DSTN). For example, the processing module sends groups ofslices to a DST EX unit, where the slices are of a common pillar numberwhen a storage method indicates dispersed storage. As another example,the processing module sends groups of slices to a DST EX unit, where theslices are of two or more pillar number when a storage method indicatesdistributed task storage to enable subsequent task execution on thepartial result. In addition, the processing module may receive moreslices for more execution steps.

FIG. 41 is a flowchart illustrating another example of storing andprocessing a group of slices, which include similar steps to FIG. 40B.The method begins with steps 354-356 of FIG. 40B where a processingmodule (e.g., of a distributed task (DT) execution module embeddedwithin a solid state memory) receives at least one partial task withregards to a group of slices of contiguous data (e.g., from a DST clientmodule) and receives slices of the group of slices to produce receivedslices.

The method continues at step 376 where the processing module stores thereceived slices in a local solid-state memory device. The random accessnature of storing the slices in the solid-state memory device mayprovide a write address agility system improvement for the group ofslices. The method continues with step 360 of FIG. 40B where theprocessing module determines whether to execute a partial task. Themethod branches to step 364 of FIG. 40B when the processing moduledetermines to execute the partial task. The method continues to step 362of FIG. 40B when the processing module determines not to execute thepartial task.

The method continues with step 362 of FIG. 40B where the processingmodule determines whether more slices are expected when the processingmodule determines not to execute a partial task. The method repeats backto step 356 of FIG. 40B when the processing module determines that thereare more slices. The method continues to step 364 of FIG. 40B when theprocessing module determines that there are no more slices.

The method continues with steps 364-366 of FIG. 40B where the processingmodule determines execution steps and schedule and identifies a portionof the contiguous data for execution of one or more steps of theexecution steps. The method continues at step 378 where the processingmodule retrieves the portion of the contiguous data from the solid-statememory device. The retrieving includes accessing a slice location tableto retrieve random access addresses within the solid-state memory devicefor the corresponding slices. The method continues with step 370 of FIG.40B where the processing module executes the steps in accordance withthe schedule on the portion of the contiguous data to produce a partialresult. The method continues at step 380 where the processing modulestores the partial result in the solid-state memory. The methodcontinues with steps 372-374 of FIG. 40B where the processing moduledispersed storage error encodes the partial results produce a pluralityof sets of slices in accordance with dispersal parameters associatedwith one or more of the group of slices and the at least one partialtask and facilitates storing a plurality of sets of slices in adispersed storage and task network (DSTN).

FIG. 42A is a schematic block diagram of another embodiment of adistributed computing system that includes a distributed storage (DS)module 382 and a distributed storage and task (DST) execution unitsuperset 384. The DS module 382 may be implemented within one or more ofa computing device, a DST execution unit 36, and a DST processing unit.The DST execution unit superset 384 includes a plurality of DSTexecution units 36. A portion of the plurality of DST execution units 36includes a set of DST execution units 386. The DS module 382 may beimplemented by one or more of a computing device, a DST client module, adistributed task (DT) execution module, a processing module, acontroller, a user device, a DST processing unit, a DST execution unit36, a distributed storage and task network (DSTN) managing unit, and aDST integrity processing unit. The DS module 382 includes a selectmodule 388, an encode module 390, a task module 392, and an outputmodule 394.

The system is operable to facilitate distributed computing of a task 398(e.g., a computing task) on data 396. The select module 388 selects theset of DST execution units 386 to produce a DST execution unit setselection for executing the task 398 on the data 396 based on at leastone of the task 398 and DST execution unit capability information 400(e.g., task execution capability level, encryption capability level,availability level). The select module 388 selects the set of DSTexecution units 386 by a series of steps. A first step includesdetermining execution requirements of the task 398. A second stepincludes analyzing, in light of the execution requirements, the DSTexecution capability information 400 for the plurality of DST executionunits 36. For example, the select module 388 receives the DST executioncapability information 400 from at least some of the plurality of DSTexecution units 36. A third step includes identifying DST executionunits 36 of the plurality of DST execution units 36 that havecapabilities corresponding to the execution requirements. A fourth stepincludes selecting the set of DST execution units 386 from theidentified DST execution units.

The encode module 390 determines dispersed storage error codingparameters for the data 396 based on information regarding the set ofDST execution units 402 (e.g., number of DST execution units in the set,storage capabilities of the set of DST execution units, task processingcapabilities of the set of DST execution units, DST execution unit setselection). The encode module 390 further functions to dispersed storageerror encode the data 396 in accordance with the dispersed storage errorcoding parameters to produce a plurality of encoded data blocks. Theencoding may include matrix multiplying an encoding matrix by portionsof the data 396 to produce the plurality of encoded data blocks. Theencoding matrix may include a unity Vandermonde matrix such that a firstdecode threshold number of encoded data blocks are substantially thesame as the data 396. The encoding may also include on-line coding orother forms of error coding. An encoded data block may include one ormore slices, an on-line encoded block, or another erasure encoded datablock.

The encode module 390 further functions to group the plurality ofencoded data blocks into a plurality of encoded data block groupings 404in accordance with the dispersed storage error encoding. The encodemodule 390 groups the plurality of encoded data blocks further bygrouping the plurality of encoded data blocks into the plurality ofencoded data block groupings 404 based on at least one of the DSTexecution unit capability information 400 and the information regardingthe set of DST execution units 402. A first grouping of encoded datablocks of the plurality of encoded data block groupings 404 may includeless encoded data blocks than a second grouping of encoded data blocksof the plurality of encoded data block groupings 404.

The task module 392 partitions the task 398 into a set of partial tasks406 based on at least one of the DST execution unit capabilityinformation 400 and the information regarding the set of DST executionunits 402. The task module 392 partitions the task 398 into the set ofpartial tasks 406 by one of a plurality of approaches. A first approachincludes replicating the task 398 for each DST execution unit of the setof DST execution units 386 to produce the set of partial tasks 406. Asecond approach includes identifying sub-tasks of the task 398 andproducing the set of partial tasks 406 based on the identifiedsub-tasks. For example, a first DST execution unit is assigned a uniquesub-task that compares favorably with a unique capability of the firstDST execution unit. A third approach includes identifying the sub-tasksof the task 398 and replicating the identified sub-tasks for each of theset of DST execution units 386 to produce the set of partial tasks 406.

The output module 394 outputs at least some of the plurality of encodeddata block groupings 408 to the set of DST execution units 386. Forexample, the output module 394 outputs a first decode threshold numberof encoded data block groupings 408 to the set of DST execution units386. The output module 394 may encrypt at least some of the encoded datablock groupings 408 to provide improved security. When encrypting, theoutput module 394 outputs the at least some of the plurality of encodeddata block groupings 408 by a series of steps. A first step includesgenerating an encryption key for an encoded data block grouping of theat least some of the plurality of encoded data block groupings 408associated with a selected DST execution unit of the set of DSTexecution units 386 based on one or more of a corresponding DSTexecution unit identifier (ID), a Diffie Hellman exchange with theselected DST execution unit, a data identifier associated with the data,and a retrieved key. For example, the output module 394 combines aretrieved base key and a DST execution unit ID of a second DST executionunit to produce a second encryption key for encrypting an encoded datablock grouping associated with the second DST execution unit. A secondstep includes encrypting the encoded data block grouping using theencryption key to produce an encrypted encoded data block grouping. Athird step includes outputting the encrypted encoded data block groupingto the selected DST execution unit. The output module 394 furtherfunctions to output the set of partial tasks 406 to the set of DSTexecution units 386 for execution of the set of partial tasks 406 on theat least some of plurality of encoded data block groupings 408.

The select module 388 further functions to identify the superset of DSTexecution units 384 for storing the plurality of encoded data blockgroupings 404, where the superset of DST execution units 384 includesthe set of DST execution units 386. The output module 394 furtherfunctions to output other encoded data block groupings 410 of theplurality of encoded data block groupings 404 to other DST executionunits of the superset of DST execution units 384. The superset of DSTexecution units 384 stores the plurality of encoded data block groupings404.

FIG. 42B is a flowchart illustrating an example of distributed computingof a task on data. The method begins at step 412 where a processingmodule (e.g., of a distributed storage and task processing unit) selectsa set of distributed storage and task (DST) execution units forexecuting the task based on at least one of the task and DST executionunit capability information. The selecting the set of DST executionunits includes a series of steps. A first step includes determiningexecution requirements of the task. A second step includes analyzing, inlight of the execution requirements, the DST execution capabilityinformation for a plurality of DST execution units. A third stepincludes identifying DST execution units of the plurality of DSTexecution units that have capabilities corresponding to the executionrequirements. A fourth step includes selecting the set of DST executionunits from the identified DST execution units.

The method continues at step 414 where the processing module determinesdispersed storage error coding parameters for the data based oninformation regarding the set of DST execution units. For example, theprocessing module establishes a pillar width to be substantially thesame as the number of DST execution units of the set of DST executionunits. The method continues at step 416 where the processing moduledispersed storage error encodes the data in accordance with thedispersed storage error coding parameters to produce a plurality ofencoded data blocks. The method continues at step 418 where theprocessing module groups the plurality of encoded data blocks into aplurality of encoded data block groupings in accordance with thedispersed storage error encoding. The grouping of the plurality ofencoded data blocks may include grouping the plurality of encoded datablocks into the plurality of encoded data block groupings based on atleast one of the DST execution unit capability information and theinformation regarding the set of DST execution units. A first groupingof encoded data blocks of the plurality of encoded data block groupingsmay include less encoded data blocks than a second grouping of encodeddata blocks of the plurality of encoded data block groupings.

The method continues at step 420 where the processing module partitionsthe task into a set of partial tasks based on at least one of the DSTexecution unit capability information and the information regarding theset of DST execution units. The partitioning the task into a set ofpartial tasks includes one of a plurality of approaches. A firstapproach includes replicating the task for each of the set of DSTexecution units to produce the set of partial tasks. A second approachincludes identifying sub-tasks of the task and producing the set ofpartial tasks based on the identified sub-tasks. A third approachincludes identifying the sub-tasks of the task and replicating theidentified sub-tasks for each of the set of DST execution units toproduce the set of partial tasks.

The method continues at step 422 where the processing module outputs atleast some of the plurality of encoded data block groupings to the setof DST execution units. The outputting the at least some of theplurality of encoded data block groupings includes a series of steps. Afirst step includes generating an encryption key for an encoded datablock grouping of the at least some of the plurality of encoded datablock groupings associated with a selected DST execution unit of the setof DST execution units based on one or more of a corresponding DSTexecution unit identifier, a Diffie Hellman exchange with the selectedDST execution unit, a data identifier associated with the data, and aretrieved key. A second step includes encrypting the encoded data blockgrouping using the encryption key to produce an encrypted encoded datablock grouping. A third step includes outputting the encrypted encodeddata block grouping to the selected DST execution unit.

The method continues at step 424 where the processing module outputs theset of partial tasks to the set of DST execution units for execution ofthe set of partial tasks on the at least some of plurality of encodeddata block groupings. The method continues at step 426 where theprocessing module identifies a superset of DST execution units forstoring the plurality of encoded data block groupings where the supersetof DST execution units includes the set of DST execution units. Themethod continues at step 428 where the processing module outputs otherencoded data block groupings of the plurality of encoded data blockgroupings to other DST execution units of the superset of DST executionunits where the superset of DST execution units stores the plurality ofencoded data block groupings.

FIG. 42C is a schematic block diagram of another embodiment of adistributed computing system that includes a distributed storage (DS)module 430 and a distributed storage and task (DST) execution unitsuperset 384. The DS module 430 may be implemented within one or more ofa computing device, a DST execution unit 36, and a DST processing unit.The DST execution unit superset 384 includes a plurality of DSTexecution units 36. A portion of the plurality of DST execution units 36includes a set of DST execution units 386. The DS module 430 may beimplemented by one or more of a computing device, a DST client module, adistributed task (DT) execution module, a processing module, acontroller, a user device, a DST processing unit, a DST execution unit,a distributed storage and task network (DSTN) managing unit, and a DSTintegrity processing unit. The DS module 430 includes an identify module432, a task module 434, and an output module 436.

The system is operable to facilitate distributed computing of a task 398(e.g., a computing task) on stored data. The identify module 432identifies the set of DST execution units 386 of the superset of DSTexecution units 384. The identify module 432 outputs a DST executionunit set information 402 that includes identity of the set of DSTexecution units 386. The superset of DST execution units 384 storestored data as a plurality of encoded data block groupings. Data isdispersed storage error encoded in accordance with dispersed storageerror coding parameters to produce a plurality of encoded data blocksthat is arranged into the plurality of encoded data block groupings. Theidentify module 432 identifies the set of DST execution units 386 by atleast one of a variety of approaches. A first approach includesdetermining a recovery threshold (e.g., decode threshold) of the databased on the dispersed storage error coding parameters and selecting anumber of DST execution units of the superset of DST execution units 384based on the recovery threshold. A second approach includes identifyingthe set of DST execution units 386 as DST execution units of thesuperset of the DST execution units 384 that is storing data slices ofthe plurality of encoded data slices. A third approach includesidentifying the set of DST execution units 386 as DST execution units ofthe superset of the DST execution units 384 where a DST execution unitof the set of DST execution units 386 is storing an encoded data blockgrouping of the plurality of encoded data block groupings and the DSTexecution unit recovers a portion of the data from the encoded datablock group.

The task module 434 partitions the task 398 for distributed computing onthe stored data into a set of partial tasks 406 based on at least one ofthe DST execution unit capability information 400 and the informationregarding the set of DST execution units 402. The task module 434functions to partition the task 398 into the set of partial tasks 406 byone of a variety of approaches. A first approach includes replicatingthe task 398 for each of the set of DST execution units 386 to producethe set of partial tasks 406. A second approach includes identifyingsub-tasks of the task 398 and producing the set of partial tasks 406based on the identified sub-tasks (e.g., each DST execution unit willreceive a unique sub-task). A third approach includes identifying thesub-tasks of the task 398 and replicating the identified sub-tasks foreach of the set of DST execution units 386 to produce the set of partialtasks 406.

The output module 436 outputs the set of partial tasks 406 to the set ofDST execution units 386 for execution of the set of partial tasks 406 ona set of the plurality of encoded data block groupings stored by the setof DST execution units. The data is recoverable from the set of theplurality of encoded data block groupings and other encoded data blockgroupings of the plurality of encoded data block groupings are for errorcoding redundancy.

FIG. 42D is a flowchart illustrating an example of distributed computingof a task on stored data. The method begins at step 438 where aprocessing module (e.g., of a distributed storage and task (DST)processing unit) identifies a set of DST execution units of a supersetof DST execution units where the superset of DST execution units store aplurality of encoded data block groupings. Data is dispersed storageerror encoded in accordance with dispersed storage error codingparameters to produce a plurality of encoded data blocks that isarranged into the plurality of encoded data block groupings. Theidentifying the set of DST execution units includes at least one of avariety of approaches. A first approach includes determining a recoverythreshold of the data based on the dispersed storage error codingparameters and selecting a number of DST execution units of the supersetof DST execution units based on the recovery threshold. A secondapproach includes identifying the set of DST execution units as the DSTexecution units of the superset of the DST execution units that isstoring data slices of the plurality of encoded data slices. A thirdapproach includes identifying the set of DST execution units as the DSTexecution units of the superset of the DST execution units, wherein aDST execution unit of the set of DST execution units is storing anencoded data block grouping of the plurality of encoded data blockgroupings and wherein the DST execution unit recovers a portion of thedata from the encoded data block group.

The method continues at step 440 where the processing module partitionsthe task into a set of partial tasks based on at least one of the DSTexecution unit capability information and the information regarding theset of DST execution units. The partitioning the task into a set ofpartial tasks includes one of a variety of approaches. A first approachincludes replicating the task for each of the set of DST execution unitsto produce the set of partial tasks. A second approach includesidentifying sub-tasks of the task and producing the set of partial tasksbased on the identified sub-tasks. A third approach includes identifyingthe sub-tasks of the task and replicating the identified sub-tasks foreach of the set of DST execution units to produce the set of partialtasks.

The method continues at step 442 where the processing module outputs theset of partial tasks to the set of DST execution units for execution ofthe set of partial tasks on a set of the plurality of encoded data blockgroupings stored by the set of DST execution units. The data isrecoverable from the set of the plurality of encoded data blockgroupings and other encoded data block groupings of the plurality ofencoded data block groupings are for error coding redundancy.

FIG. 43A is a schematic block diagram of another embodiment of adistributed computing system that includes a computing device 444 and aplurality of distributed storage and task (DST) execution units 446. Theplurality of DST execution units 446 includes at least one set of DSTexecution units 448 that each includes two or more DST execution units36. The computing device 444 may be implemented by one or more of a DSTexecution unit 36 of the plurality of DST execution units 446, a DSTclient module, a distributed task (DT) execution module, a processingmodule, a controller, a user device, a DST processing unit, adistributed storage and task network (DSTN) managing unit, and a DSTintegrity processing unit. For example, the computing device 444 isimplemented as a DST execution unit 36 of the DST execution unit set448. The computing device 444 includes a distributed storage (DS) module450 and a local memory 458. The local memory 458 may be implementedutilizing one or more memory devices. A memory device of the one or morememory devices may be implemented utilizing one or more of a solid-statememory, an optical disk drive, and a magnetic disk drive. The DS module450 includes a receive module 452, a task module 454, and a storagemodule 456.

The system is operable to perform a partial task 460 (e.g., a computingtask) on an encoded data block grouping 462. The partial task 460 mayinclude programming instructions to execute the partial task and mayinclude a command that identifies a set of instructions to be evoked.Data is dispersed storage error encoded in accordance with dispersedstorage error coding parameters to produce a plurality of encoded datablocks. The plurality of encoded data block groupings includes theencoded data block grouping 462. The receive module 452 receives thepartial task 460 regarding the encoded data block grouping 462 of theplurality of encoded data block groupings. The receive module 452receives the partial task by a series of steps when the partial task isencoded utilizing a dispersed storage error coding function. A firststep includes receiving a set of encoded task data blocks 464. Forexample, the receive module 452 retrieves the set of encoded task datablocks 464 from the DST execution unit set 448. A second step includesdetermining dispersed store error decoding parameters regarding thepartial task. The determining includes at least one of initiating aquery, retrieving, receiving, and a look up. A third step includesdecoding the set of encoded task data blocks 464 to recover the partialtask 460. Alternatively, or in addition to, the receive module 452receives the encoded data block grouping 462 (e.g., from a DSTprocessing unit) and/or retrieves the encoded data block grouping 462from the local memory 458.

The task module 454 performs the partial task 460 on the encoded datablock grouping 462 to produce a partial task result 466. The task moduleperforms the partial task 460 by decoding the encoded data blockgrouping 462 in accordance with the dispersed storage error codingparameters to produce a partition of the data and performing the partialtask 460 on the partition of data to produce the partial task result466. The task module further performs the partial task by configuring anexecution unit (e.g., a DST execution unit 36, the DS module 450, etc.)to perform the partial task 460 based on content of the partial task460. For example, the task module decodes the encoded data blockgrouping 462 to produce a slice as the partition of the data.

The task module 454 further functions to determine whether the encodeddata block grouping 462 is encrypted. The determining may be based onone or more of a flag, a test, receiving a message, and a look up. Whenthe encoded data block grouping 462 is encrypted, the task module 454determines sensitivity of the partition of data. The determining may bebased on one or more of a lookup, a query, and receiving thesensitivity. When the sensitivity of the partition of data is of a firstsensitivity, the task module 454 decrypts the encoded data blockgrouping 462 to produce the encoded data block grouping 462 andtemporarily stores the partition of data. When the partial task 460 hasbeen performed, the task module 454 deletes the temporary storage of thepartition of data (e.g., overwrite with another value, zero out).

The task module 454 further functions to perform the partial task 460 bydetermining the DS module's 450 ability to fulfill the partial task 460in a reasonable time frame. When the task module 454 cannot fulfill thepartial task 460 in the reasonable time frame, the task module 454executes a series of steps. A first step includes partitioning thepartial task 460 into a set of sub-partial tasks. A second step includesportioning the encoded data block grouping 462 into a set of encodeddata block sub-groupings. A third step includes sending a request 468that includes the set of sub-partial tasks and set of encoded data blocksub-groupings to the set of DST execution units 448 of the distributedcomputing system. The set of DST execution units 448 may include thecomputing device 444. A fourth step includes receiving sub-partial taskresults 470 from the set of DST execution units 448. A fifth stepincludes compiling the sub-partial task results 470 to produce thepartial task result 466.

The storage module 456 determines subsequent treatment of the partialtask result 466 and of the encoded data block grouping 462. The storagemodule 456 functions to determine the subsequent treatment by at leastone of a plurality of approaches. A first approach includes extractingsubsequent treatment information from the partial task 460. A secondapproach includes analyzing the partial task result 460 for one or moreresult criteria to determine the subsequent treatment (e.g., size,sensitivity, user identity, source, destination, analytics type ofresult of the source data, a modification type of result of the sourcedata). A third approach includes sending a query to another devicewithin a distributed computing system to ascertain the subsequenttreatment. A fourth approach includes analyzing the encoded data blockgrouping 462 to identify source data criteria (e.g., sensitivity, useridentity, source, destination, etc.) to determine the subsequenttreatment.

When the subsequent treatment includes maintaining storage of theencoded data block grouping 462 and storage of the partial task result466, the storage module 456 determines a manner in which the partialtask result 466 is to be stored (e.g., store locally, store in at leastsome of the plurality DST execution units 446). When the manner in whichthe partial task result 466 is to be stored is dispersed storage, thestorage module 456 dispersed storage error encodes the partial taskresult 466 in accordance with dispersed storage error encodingparameters to produce one or more sets of encoded partial task resultblocks 472. Next, the storage module outputs the one or more sets ofencoded partial task result blocks 472 to the set of DST execution units448 for storage therein.

When the manner in which the partial task result 466 is to be stored isstore locally, the storage module 456 stores the partial task result 466in the local memory 458. When the manner in which the partial taskresult 466 is to be stored is group dispersed storage, the storagemodule 456 coordinates with other DST execution units 36 of thedistributed computing system to collect a set of partial task resultswhere the set of partial task results includes the partial task result466 and partial task results of the other DST execution units 36 thatperformed a corresponding partial task on other encoded data blockgroupings of the plurality of encoded data block groupings. Next, thestorage module 456 disperse storage error encodes the set of partialresults to produce an error encoded result data block.

When the subsequent treatment includes overwriting the encoded datablock grouping 462 with the partial task result 466, the storage module456 overwrites the encoded data block grouping 462 with the partial taskresult 466 (e.g., within the local memory). Next, the storage module 456coordinates with other DST execution units 36 of the distributedcomputing system to update redundancy data blocks 474 of the pluralityof encoded data block groupings based on the partial task results. Thecoordinating includes identifying an update approach and facilitatingthe updating of the redundancy data blocks 474. The update approachincludes encoding a decode threshold number of encoded data blockgroupings to produce modified redundancy data blocks 474. Theidentifying may be based on one or more of the predetermination, alookup, receiving a message, distributed computing system capacity, anda network loading level. The facilitating the updating includesassigning one or more DST execution units 36 of the other DST executionunits to perform the encoding.

FIG. 43B is a flowchart illustrating an example of performing a partialtask. The method begins at step 476 where a processing module (e.g., ofa dispersed storage and task (DST) execution unit) receives a partialtask regarding an encoded data block grouping of a plurality of encodeddata block groupings. Data is dispersed storage error encoded inaccordance with dispersed storage error coding parameters to produce theplurality of encoded data blocks. The receiving the partial taskincludes a series of steps. A first step includes receiving a set ofencoded task data blocks. A second step includes determining dispersedstore error decoding parameters regarding the partial task. A third stepincludes decoding the set of encoded task data blocks to recover thepartial task.

The method continues at step 478 where the processing module performsthe partial task on the encoded data block grouping to produce a partialtask result. The performing the partial task includes configuring anexecution unit to perform the partial task based on content of thepartial task. The performing the partial task further includesdetermining the processing module's ability to fulfill the partial taskin a reasonable time frame. When the processing module cannot fulfillthe partial task in the reasonable time frame, the processing moduleexecutes a series of steps. A first step includes partitioning thepartial task into a set of sub-partial tasks. A second step includesportioning the encoded data block grouping into a set of encoded datablock sub-groupings. A third step includes sending the set ofsub-partial tasks and set of encoded data block sub-groupings to a setof DST execution units of a distributed computing system. A fourth stepincludes receiving sub-partial task results from the set of DSTexecution units. A fifth step includes compiling the sub-partial taskresults to produce the partial task result.

The performing the partial task further includes decoding the encodeddata block grouping in accordance with the dispersed storage errorcoding parameters to produce a partition of the data and performing thepartial task on the partition of data to produce the partial taskresult. The performing the partial task further includes determiningwhether the encoded data block grouping is encrypted and when theencoded data block grouping is encrypted determining sensitivity of thepartition of data. When the sensitivity of the partition of data is of afirst sensitivity, the processing module decrypts the encoded data blockgrouping to produce the encoded data block grouping, temporarily storesthe partition of data, and when the partial task has been performed,deletes the temporary storage of the partition of data.

The method continues at step 480 where the processing module determinessubsequent treatment of the partial task result and of the encoded datablock grouping. The determining the subsequent treatment includes atleast one of a variety of approaches. A first approach includesextracting subsequent treatment information from the partial task. Asecond approach includes analyzing the partial task result for one ormore result criteria to determine the subsequent treatment. A thirdapproach includes sending a query to another device within a distributedcomputing system to ascertain the subsequent treatment. A fourthapproach includes analyzing the encoded data block grouping to identifysource data criteria to determine the subsequent treatment. The methodbranches to step 486 when the processing module determines thesubsequent treatment to include storing the partial task result. Themethod continues to step 482 when the processing module determines thesubsequent treatment to include overwriting the encoded data blockgrouping.

When the subsequent treatment includes overwriting the encoded datablock grouping with the partial task result, the method continues atstep 482 where the processing module overwrites the encoded data blockgrouping with the partial task result. The method continues at step 484where the processing module coordinates with other DST execution unitsof a distributed computing system to update redundancy data blocks ofthe plurality of encoded data block groupings based on the partial taskresults.

When the subsequent treatment includes maintaining storage of theencoded data block grouping and storage of the partial task result, themethod continues at step 486 where the processing module determines amanner in which the partial task result is to be stored. When the mannerin which the partial task result is to be stored is dispersed storage,the method continues at step 488 where the processing module dispersedstorage error encodes the partial task result in accordance withdispersed storage error encoding parameters to produce one or more setsof encoded partial task result blocks. The method continues at step 490where the processing module outputs the one or more sets of encodedpartial task result blocks to another set of DST execution units forstorage therein.

When the manner in which the partial task result is to be stored isstore locally, the method continues at step 492 where the processingmodule stores the partial task result in local memory of the DSTexecution unit. When the manner in which the partial task result is tobe stored is group dispersed storage, the method continues at step 494where the processing module coordinates with other DST execution unitsof the distributed computing system to collect a set of partial taskresults where the set of partial task results includes the partial taskresult and partial task results of the other DST execution units thatperformed a corresponding partial task on other encoded data blockgroupings of the plurality of encoded data block groupings. The methodcontinues at step 496 where the processing module disperse storage errorencodes the set of partial results to produce an error encoded resultdata block.

FIG. 44A is a diagram of an example embodiment of a distributed storageand task (DST) unit 500 that includes a controller 86, a memory 88, adistributed task (DT) execution module A, a DT execution module B, and aDST client module 34. The DT execution module A and DT execution moduleB may be implemented utilizing one or more modules. The DST clientmodule 34 includes at least one of an inbound DST processing 82 and anoutbound DST processing. The DST unit 500 ingests raw data 502 forstorage and processing in accordance with a received task 94. The task94 includes one or more of a raw data search task and a partial task forexecution on slices sent to the DST unit 500 (e.g., storage and/orprocessing).

The controller 86 produces control information based on the task 94 tocontrol one or more of the memory 88, DT execution module A, DTexecution module B, and the DST client module 34. For example, thecontroller 86 produces a memory control 174 such that the memory 88caches the raw data 502 and generates index generation task information508 such that DT execution module A processes the raw data 502 inaccordance with the index generation task information 508 to produce adata index 504. The index generation task information 508 includes oneor more of a search parameter, a keyword, pattern recognitioninformation, and timing information. The data index 504 includesmetadata of the raw data 502 including one or more of keywords, dates,internet protocol addresses, partial content, word counts, statistics, asummary, a distributed storage and task network (DSTN) addresscorresponding to raw data storage, a DSTN address corresponding to dataindex storage, and a DSTN address corresponding to index data storage.

The controller 86 may also generate data indexing task information 510with regards to indexing of the data index 504. The data indexing taskinformation 510 includes one or more of data reduction instructions, akeyword filter, a data index reference, and an indexed data format. TheDT execution module B processes the raw data 502 in accordance with thedata indexing task information 510 to produce indexed data 506. Theindexed data 506 includes a subset of the raw data 502 organized inaccordance with the data index 504.

The controller 86 controls the memory 88 with the memory control 174 tofacilitate caching one or more of the raw data 502, the data index 504,and the indexed data 506. The memory control 174 may also facilitate thememory 88 outputting one or more of the raw data 502, the data index504, and the indexed data 506. The memory control 174 may alsofacilitate the memory 88 inputting slice groupings 96 for caching in thememory 88 to facilitate further processing by DT execution module Aand/or B.

The controller 86 generates and outputs a DST control 178 to the DSTclient module 34 to facilitate the generation and outputting of one ormore of slice groupings 96 of the raw data 502, of the data index 504,of the indexed data 506, and one or more partial tasks 98. For example,the DST client module 34 sends a portion of the slice groupings 96 ofthe raw data 502 to the memory 88 for storage and sends other portionsof the slice groupings 96 to other DST units for storage therein. Asanother example, the DST client module 34 generates slice groupings 96of the indexed data 506 and sends the slice groupings 96 of indexed data506 to at least one other DST unit for further processing (e.g., apattern search).

FIG. 44B is a schematic block diagram of another embodiment of adistributed computing system that includes a computing device 512, anetwork 530, and a plurality of distributed storage and task (DST) units514. The network 530 includes one or more of a computing network, acommunication network, a processing network, a storage network, and anynetwork capable of one or more of storing data, communicating data,sourcing data, consuming data, and processing data. The plurality of DSTunits 514 includes a first set of DST units 516 and a second set of DSTunits 518 that each includes two or more DST units 519. A DST unit 519of the plurality of DST units 514 may be implemented by one or more of aDST execution unit, a server, the user device, and a DST processingunit. The computing device 512 may be implemented by one or more of aDST unit 519 of the plurality of DST units 514, a DST execution unit, aDST client module, a distributed task (DT) execution module, aprocessing module, a controller, a user device, a DST processing unit, adistributed storage and task network (DSTN) managing unit, and a DSTintegrity processing unit. The computing device 512 includes adistributed storage (DS) module 520. The DS module 520 includes anidentifying criteria module 522, an analyzing criteria module 524, adistributed computing criteria module 526, and a results module 528.

The system is operable to facilitate searching data 532 on the network530 to produce found data 534 and to analyze the found data 534 toproduce a network data resultant 536. The identifying criteria module522 establishes data identifying criteria 538 for searching data on anetwork. The identifying criteria module 522 establishes the dataidentifying criteria 538 by at least one of a plurality of approaches. Afirst approach includes determining data content search criteria (e.g.,text search words, phrase search, photo, voice print, etc.). A secondapproach includes determining file name search criteria. A thirdapproach includes determining data source identifying criteria. A fourthapproach includes determining data destination identifying criteria. Afifth approach includes determining data type searching criteria (e.g.,text file, email file, picture file, video file, etc.). A sixth approachincludes determining data routing searching criteria (e.g., a datageneration source, a data generation consumption entity, a routing path,an alternate routing path, etc.). A seventh approach includes compilingat least one of the data content search criteria, the file name searchcriteria, the data source identifying criteria, the data destinationidentifying criteria, the data type searching criteria, and the datarouting searching criteria to produce the data identifying criteria 538.

The analyzing criteria module 524 establishes data analyzing criteria540 for analyzing found data 534 of the data 532 on the network 530. Theanalyzing criteria module 524 establishes data analyzing criteria 540 byat least one of a plurality of approaches. A first approach includesperforming organization analysis on the found data 534 (e.g., filteringlimit found data content to a particular sender and/or recipient,categorizing the found data, aggregating the found data, etc.). A secondapproach includes performing statistical analysis on the found data 534(e.g., word count, number of matches, summary of found data, etc.). Athird approach includes performing interpretive analysis on the founddata 534 (e.g., hidden means, translation, error detection, etc.).

The distributed computing criteria module 526 establishes distributedcomputing criteria 542 based on the data identifying criteria and dataanalyzing criteria. The distributed computing criteria module 526establishes the distributed computing criteria 542 by at least one of aplurality of approaches. A first approach includes estimatingcomputational resource requirements for searching the data on thenetwork in accordance with the data identifying criteria 538 to producethe found data 534 and analyzing the found data 534 in accordance withthe data analyzing criteria 540. A second approach includes determiningcomputational capabilities of the set of DST units 516. A third approachincludes establishing the distributed computing criteria 542 based onthe estimated computational resource requirements and the computationalcapabilities.

The results module 528 facilitates searching the data 532 to produce thefound data 534 and analyzing the found data 534 to produce the networkdata resultant 536 through a series of steps. In a first step, theresults module 528 distributes the data identifying criteria 538 and thedata analyzing criteria 540 to the set of DST units 516 in accordancewith the distributed computing criteria 542. The distributing mayinclude the results module 528 estimating computational resourcerequirements for searching the data 532 on the network 530 in accordancewith the data identifying criteria 538 to produce the found data 534 andanalyzing the found data 534 in accordance with the data analyzingcriteria 540. Next, the results module 528 selects the set of DST units516 from the plurality of DST units 514 based on the computationalresource requirements.

In a second step, the results module 528 receives a set of network datapartial resultants 544 from the set of DST units 516 where the set ofDST units 516 generates the set of network data partial resultants 544based on searching the data 532 on the network 530 in accordance withthe data identifying criteria 538 to produce the found data 534 andanalyzing the found data 534 in accordance with the data analyzingcriteria 540. In a third step, the results module 528 processes the setof network data partial resultants 544 to produce the network dataresultant 536 regarding the data 532 on the network 530.

The results module 528 further functions to establish data storagecriteria for storing at least one of the found data 534 and the data 532on the network 530. The data storage criteria identifies the at leastone of the found data 534 and the data 532 on the network 530 andincludes dispersed storage error coding parameters. The set of DST units516 further functions to store the at least one of the found data 534and the data 532 on the network 530 in accordance with the dispersedstorage error coding parameters. For example, the results module 528dispersed storage error encodes the at least one of the found data 534and the data 532 on the network 530 in accordance with the dispersedstorage error coding parameters to produce a plurality of sets ofencoded data slices 546 and outputs the plurality of sets of encodeddata slices 546 to the set of DST units 516 for storage therein.

A DST unit 519 of the set of DST units 516 may determine whether tosub-distribute at least one of an allocated portion of the network dataidentifying criteria 538 and an allocated portion of the network dataanalyzing criteria 540. When the DST unit 519 determines tosub-distribute the at least one of an allocated portion of the networkdata identifying criteria 538 and an allocated portion of the networkdata analyzing criteria 540, the DST unit 519 establishes at least oneof local data identifying criteria 548 for searching the data 532 on thenetwork 530 based on an allocation portion of the data identifyingcriteria 538 and local data analyzing criteria 550 for analyzing localfound data 552 of the found data 534 based on an allocation portion ofthe data analyzing criteria 540. The DST unit establishes localdistributed computing criteria based on at least one of the local dataidentifying criteria 548 and the local data analyzing criteria 550. TheDST unit 519 distributes the at least one of the local data identifyingcriteria 548 and the local data analyzing criteria 550 to a second setof DST units 518 in accordance with the local distributed computingcriteria (e.g., second set may include this DST unit).

At least one of the DST unit 519 of the first set of DST units 516 andthe second set of DST units 518 performs analysis of local found data552 to produce one of the set of network data partial resultants 544.When the DST unit 519 of the set of DST units 516 performs analysis oflocal found data 552, the DST unit 519 of the set of DST units 516performs a series of steps. A first step includes distributing the localdata identifying criteria 548 to the second set of DST units 518 inaccordance with the local distributed computing criteria. A second stepincludes receiving a set of sub-partial found data 554 from the secondset of DST units 518. A third step includes compiling the set ofsub-partial found data 554 into partial found data. A fourth stepincludes performing the allocation portion of the data analyzingcriteria 540 on the partial found data to produce one of the set ofnetwork data partial resultants 544.

When the second set of DST units 518 performs analysis of local founddata 552, the DST unit 519 of the set of DST units 516 performs a seriesof steps. A first step includes performing the allocation portion of thedata identifying criteria 538 to produce partial found data. A secondstep includes distributing the local data analyzing criteria 550 to thesecond set of DST units 518 in accordance with the local distributedcomputing criteria. A third step includes dividing the partial founddata into a set of sub-partial found data 554 in accordance with thelocal distributed computing criteria. A fourth step includesdistributing the set of sub-partial found data 554 to the second set ofDST units 518 in accordance with the local distributed computingcriteria. A fifth step includes receiving a set of data sub-partialresults 556 from the second set of DST units 518. A sixth step includescompiling the set of data sub-partial results 556 to produce one of theset of network data partial resultants 544.

FIG. 44C is a flowchart illustrating an example of analyzing data. Themethod begins at step 560 where a processing module (e.g., of adispersed storage and task (DST) unit) establishes data identifyingcriteria for searching data on a network. The establishing includes atleast one of a plurality of approaches. A first approach includesdetermining data content search criteria (e.g., text search words,phrase search, photo, voice print, etc.). A second approach includesdetermining file name search criteria. A third approach includesdetermining data source identifying criteria. A fourth approach includesdetermining data destination identifying criteria. A fifth approachincludes determining data type searching criteria (e.g., text file,email file, picture file, video file, etc.). A sixth approach includesdetermining data routing searching criteria (e.g., a data generationsource, a data generation consumption entity, a routing path, analternate routing path, etc.). A seventh approach includes compiling atleast one of the data content search criteria, the file name searchcriteria, the data source identifying criteria, the data destinationidentifying criteria, the data type searching criteria, and the datarouting searching criteria to produce the data identifying criteria.

The method continues at step 562 where the processing module establishesdata analyzing criteria for analyzing found data of the data on thenetwork. The establishing includes at least one of a plurality ofapproaches. A first approach includes performing organization analysison the found data (e.g., filtering limit found data content to aparticular sender and/or recipient, categorizing the found data,aggregating the found data, etc.). A second approach includes performingstatistical analysis on the found data (e.g., word count, number ofmatches, summary of found data, etc.). A third approach includesperforming interpretive analysis on the found data (e.g., hidden means,translation, error detection, etc.).

The method continues at step 564 where the processing module establishesdistributed computing criteria based on the data identifying criteriaand data analyzing criteria. The establishing includes at least one of aplurality of approaches. A first approach includes estimatingcomputational resource requirements for searching the data on thenetwork in accordance with the data identifying criteria to produce thefound data and analyzing the found data in accordance with the dataanalyzing criteria. A second approach includes determining computationalcapabilities of a set of DST units. A third approach includesestablishing the distributed computing criteria based on the estimatedcomputational resource requirements and the computational capabilities.

The method continues at step 566 where the processing module distributesthe data identifying criteria and the data analyzing criteria to a setof distributed storage and task (DST) units in accordance with thedistributed computing criteria. The distributing may include estimatingcomputational resource requirements for searching the data on thenetwork in accordance with the data identifying criteria to produce thefound data and analyzing the found data in accordance with the dataanalyzing criteria. Next, the processing module selects the set of DSTunits from a plurality of DST units based on the computational resourcerequirements.

The method continues at step 568 where a DST unit of the set of DSTunits determines whether to sub-distribute at least one of an allocatedportion of the network data identifying criteria and an allocatedportion of the network data analyzing criteria. The DST unit determinesto sub-distribute at least one of an allocated portion of the networkdata identifying criteria and an allocated portion of the network dataanalyzing criteria when offloading at least one of identifying data andanalyzing data to a second set of DST units. For example, the DST unitdetermines to offload the identifying data when available DST unitresources compares unfavorably to a required level of resources. The DSTunit performs the identifying data and analyzing data when notoffloading to output a network data partial resultant (e.g., to theprocessing module).

When the DST unit determines to sub-distribute the at least one of anallocated portion of the network data identifying criteria and anallocated portion of the network data analyzing criteria, the methodcontinues at step 570 where the processing module establishes at leastone of local data identifying criteria for searching the data on anetwork based on an allocation portion of the data identifying criteriaand local data analyzing criteria for analyzing local found data of thefound data based on an allocation portion of the data analyzingcriteria. Next, the DST unit establishes local distributed computingcriteria based on at least one of the local data identifying criteriaand the local data analyzing criteria. The method continues at step 572where the DST unit distributes the at least one of the local dataidentifying criteria and the local data analyzing criteria to the secondset of DST units in accordance with the local distributed computingcriteria.

When the DST unit offloads the identifying, the method continues at step574 where the DST unit receives a set of sub-partial found data from thesecond set of DST units subsequent to distributing the local dataidentifying criteria to the second set of DST units in accordance withthe local distributed computing criteria. The method continues at step576 where the DST unit compiles the set of sub-partial found data intopartial found data. The method continues at step 578 where the DST unitperforms the allocation portion of the data analyzing criteria on thepartial found data to produce one of the set of network data partialresultants. Next, the DST unit outputs the one of the set of networkdata partial resultants (e.g., to the processing module). The methodbranches to step 592.

When the DST unit offloads the analyzing, the method continues at step580 where the DST unit performs the allocation portion of the dataidentifying criteria to produce partial found data. The method continuesat step 582 where the DST unit distributes the local data analyzingcriteria to the second set of DST units in accordance with the localdistributed computing criteria. The method continues at step 584 wherethe DST unit divides the partial found data into a set of sub-partialfound data in accordance with the local distributed computing criteria.The method continues at step 586 where the DST unit distributes the setof sub-partial found data to the second set of DST units in accordancewith the local distributed computing criteria. The method continues atstep 588 where the DST unit receives a set of data sub-partial resultsfrom the second set of DST units. The method continues at step 590 wherethe DST unit compiles the set of data sub-partial results to produce oneof the set of network data partial resultants. Next, the DST unitoutputs the one of the set of network data partial resultants (e.g., tothe processing module).

The method continues at step 592 where the processing module receives aset of network data partial resultants from the set of DST units,wherein the set of DST units generates the set of network data partialresults based on searching the data on the network in accordance withthe data identifying criteria to produce the found data and analyzingthe found data in accordance with the data analyzing criteria. Themethod continues at step 594 where the processing module processes theset of network data partial resultants to produce a network dataresultant regarding the data on the network. The method continues atstep 596 where the processing module establishes data storage criteriafor storing at least one of the found data and the data on a network,wherein the data storage criteria identifies the at least one of thefound data and the data on a network and includes dispersed storageerror coding parameters. The method continues at step 598 where the setof DST units store the at least one of the found data and the data on anetwork in accordance with the dispersed storage error codingparameters.

FIG. 45 is a flowchart illustrating an example of searching a dataindex. The method begins at step 600 where a processing module (e.g., ofa distributed storage and task (DST) execution unit) obtains a dataindex search request to search a data index. The data index searchrequest includes one or more of a data index identifier of a data indexto search, one or more search terms (e.g., a trigger, a pattern, avalue, a range, match criteria, failure criteria), subsequent searchterms for subsequent searches based on a search term match, andsubsequent search terms for subsequent searches based on an unfavorablesearch term match. The obtaining includes one or more of receiving,determining based on a previous data index search (e.g., modify a searchterm based on a previous result), a predetermination, a query, and alist.

The method continues at step 602 where the processing module identifiesa portion of the data index to search based on the request. Theidentifying may be based on one or more of the request, a data indexdirectory (e.g., a mapping of major subsections of the data index),execution resource availability, and a search timeframe requirement. Themethod continues at step 604 where the processing module identifies adispersed storage and task network (DSTN) storage location correspondingto the portion. The storage location may include a local location (e.g.,storage in a memory associated with a present DST execution unit) andone or more other DST execution units. The identifying may be based onone or more of the portion, a directory lookup, a query, and receivingstorage location information.

The method continues at step 606 where the processing module determineswhether the DSTN storage location is local. The determining may be basedon one or more of a directory lookup, a query, and a memory map. Themethod branches to step 610 when the processing module determines thatthe DSTN storage location is not local. The method continues to 608 whenthe processing module determines that the DSTN storage location islocal. The method continues at step 608 where the processing modulesearches the portion of the data index to produce a result (e.g.,executes a search task).

The method continues at step 610 where the processing module generates atask request when the processing module determines that the DSTN storagelocation is not local. The generating is based on one or more of thedata index search request, the portion of the data index to search, andthe DSTN storage location. For example, the processing module generatestwo task requests that include the search task and two DSTN addressescorresponding to the DSTN storage location at two DST execution units.The method continues at step 612 where the processing module sends thetask request to a DST execution unit associated with the storagelocation. The method continues at step 614 where the processing modulereceives a result (e.g., from the DST execution unit associated with thestorage location).

FIG. 46 is a flowchart illustrating another example of searching a dataindex, which includes similar steps to FIG. 45. The method begins withsteps 600, 602, and 608 of FIG. 45 where a processing module (e.g., of adistributed storage and task (DST) execution unit) obtains a data indexsearch request to search a data index, identifies a portion of the dataindex to search based on the request, and searches the portion of thedata index to produce a result. The method continues at step 614 wherethe processing module determines whether the result is favorable. Afavorable result corresponds to a result that compares favorably to adesired result (e.g., a search successfully found a search item, apattern recognition successfully matched a pattern, etc.). The methodbranches to step 618 when the processing module determines that theresult is not favorable. The method continues to step 616 when theprocessing module determines that the result is favorable. The methodcontinues at step 616 where the processing module outputs the result.The outputting includes one or more of generating a partial result andsending the partial result to a requesting entity.

The method continues at step 618 where the processing module determineswhether to modify the data index. The determining may include comparinga difference between the result and an expected result to a resultthreshold and indicating that the data index shall be modified when thedifference is larger than the result threshold. The method branches tostep 624 when the processing module determines to not modify the dataindex. The method continues to step 620 when the processing moduledetermines to modify the data index.

The method continues at step 620 where the processing module generatesupdated index generation task information based on the result whenmodifying the data index. The updated index generation task informationincludes information to re-index raw data to produce an updated dataindex and to produce a more favorable result. The generating may bebased on one or more of the result, an unfavorable attribute of theresult, and the data index. The method continues at step 622 where theprocessing module indexes the raw data in accordance with the indexgeneration task information to produce an updated data index. Inaddition, the processing module may send a task request to another DSTexecution unit. The method branches to step 624.

The method continues at step 624 where the processing module modifiesthe data index search request. The modifying may be based on one or moreof the result, a data index identifier, and an unfavorable attribute ofthe result. The method repeats back to step 602 of FIG. 45.

FIG. 47A is a flowchart illustrating an example of initiating thresholdcomputing, which includes similar steps to FIG. 5. The method beginswith step 126 of FIG. 5 where a processing module (e.g., of adistributed storage and task (DST) client module) receives data and acorresponding task. The method continues at step 626 where theprocessing module selects one or more DST execution units for the taskbased on a capability level associated with each of the DST executionunits. The selecting includes one or more of determining a number of DSTexecution units and selecting the number of DST execution units based onone or more of an estimated distributed computing loading level, a DSTexecution unit capability indicator, a DST execution unit performanceindicator, a DST execution unit availability level indicator, a taskschedule, and a DST execution unit threshold computing capabilityindicator. For example, the processing module selects DST executionunits 1-8 when DST execution unit availability level indicators for DSTexecution units 1-8 compares favorably to an estimated distributedcomputing loading level. The method continues with step 130 of FIG. 5where the processing module determines processing parameters of the databased on a number of DST execution units.

The method continues at step 628 where the processing module determinestask partitioning based on the DST execution units, the processingparameters, and a threshold computing parameter. The threshold computingparameter includes one or more of a decode threshold number of DSTexecution units, a width number of DST execution units, and a taskredundancy requirement (e.g., a number of DST execution units to executean identical partial task). For example, the processing modulepartitions the task evenly into five partial tasks to assign to five ofeight DST execution units when the decode threshold number is five andthe width number is eight. The method continues with steps 134-136 ofFIG. 5 where the processing module processes the data in accordance withthe processing parameters to produce slice groupings and partitions thetask based on the task partitioning to produce partial tasks.

The method continues at step 630 where the processing module sends theslice groupings and corresponding partial tasks to the selected DSTexecution units. The method continues at step 632 where the processingmodule determines whether a decode threshold number of partial resultsare available. The determining may be based on one or more of receivinga partial result, receiving a partial result status, a query, retrievinga partial result, and comparing a number of partial results to thedecode threshold. The method continues at step 634 where the processingmodule obtains at least the decode threshold number of partial resultsbased on the determining whether the decode threshold number of partialresults are available. The obtaining includes one or more of receiving apartial result, determining distributed storage and task network (DSTN)addresses corresponding to the selected DST execution units, generatingat least a decode threshold number of partial result requests, andsending the at least the decode threshold number of partial resultrequests to the selected DST execution units utilizing the correspondingDSTN addresses. The method continues at step 636 where the processingmodule processes the decode threshold number of partial results toproduce a result. The processing includes at least one of aggregatingthe partial results and decoding the partial results to produce theresult.

FIG. 47B is a flowchart illustrating an example of processing athreshold computing task, which includes similar steps to FIG. 40B. Themethod begins with step 354 of FIG. 40B where a processing module (e.g.,of a distributed storage and task (DST) execution unit) receives atleast one partial task with regards to a group of slices of contiguousdata. The method continues at step 638 where the processing modulereceives the group of slices. The method continues with steps 364, 366,and 370 of FIG. 40B where the processing module determines executionsteps of schedule, identifies a portion of the contiguous data, andexecutes the steps in accordance with the schedule on the portion of thecontiguous data to produce a partial result.

The method continues at step 640 where the processing module determineswhether the partial result compares favorably to an expected result. Theexpected result includes one or more of a result that was produced, theresult was produced without computing errors (e.g., no divide by zero,etc.), the result is within a predetermined favorable range of results,and a result type of the result is of a predetermined result type. Themethod branches to step 642 when processing module determines that thepartial result compares favorably to the expected result. The methodcontinues to step 654 when the processing module determines that thepartial result compares unfavorably to the expected result. The methodcontinues at step 654 where the processing module modifies the executionsteps and schedule. The modifying includes one or more of establishingupdated steps and/or schedule to address an unfavorable nature of thepartial result. The method loops back to step 366 of FIG. 40B.Alternatively, the process may end when reaching a limit of a number ofloops and/or receiving a cancel request.

The method continues at step 642 where the processing module indicatesthat the partial result is favorable when the processing moduledetermines that the partial result compares favorably to the expectedresult. For example, the processing module sends a result status to arequesting entity that includes an indication that the partial result isfavorable. The method continues at step 644 where the processing modulegenerates a slice grouping of the partial result. The method continuesat step 646 where the processing module generates error coded data slicegroupings modification information based on the slice groupings of thepartial result. The generating may be based on one or more of a numberof participating pillars, the slice grouping, a previous slice groupingof the partial result, an encoding matrix, an error coded data pillarnumber, and a zero information gain slice building approach. The zeroinformation gain slice rebuilding approach is discussed in greaterdetail with reference to FIG. 50.

The method continues at step 648 where the processing module facilitatesstoring the slice grouping in the DSTN. For example, the processingmodule stores the slice grouping in a memory associated with a local(e.g., present) DST execution unit. The method continues at step 650where the processing module facilitates storing the error coded dataslice grouping modification information in the DSTN. For example, theprocessing module sends a first error coded data slice groupingmodification information to a first DST execution unit and a seconderror coded data slice grouping modification information to a second DSTexecution unit, where the first and second DST execution units storeerror coded data slices corresponding to the slice grouping. The methodcontinues at step 652 where the processing module indicates that thepartial result is available. For example, the processing module sends aresult status to the requesting entity that includes an indication thatthe partial result is available (e.g., available in the DSTN forretrieval).

FIG. 48A is a flowchart illustrating an example of generating a task,which includes similar steps to FIGS. 5 and 47A. The method begins withstep 126 of FIG. 5 where a processing module (e.g., of a distributedstorage and task (DST) client module) receives data and a correspondingtask and continues with step 626 of FIG. 47A where the processing moduleselects one or more DST execution units for the task based on acapability level associated with each of the DST execution units. Themethod continues with steps 130-136 of FIG. 5 where the processingmodule determines processing parameters of the data based on a number ofDST execution units, determines task partitioning based on the DSTexecution units and the processing parameters, processes the data inaccordance with the processing parameters to produce slice groupings,and partitions the task based on the task partitioning to producepartial tasks.

The method continues at step 654 where the processing module generates apartial task request message for each DST execution unit that includescorresponding partial tasks. As such, a mailbox message is producedcorresponding to each partial task request message. The method continuesat step 656 where the processing module processes each partial taskrequest message in accordance with the processing parameters to producetask request slice groupings. The processing includes generating slicessuch that each message is directed at a corresponding DST executionunit. The method continues at step 658 where the processing module sendsthe slice groupings and the task request slice groupings to the selectedDST execution units for storage therein. For example, the processingmodule sends a second slice grouping and a second task request slicegrouping to DST execution unit 5, wherein the second slice groupingcorresponds to the second task request slice grouping.

The method continues at step 660 where the processing module retrievesat least a decode threshold number of task response slices of one ormore task response slice groupings from the DST execution units. Theretrieving includes one or more of generating a retrieval request forslices that are of contiguous bytes of a task response and sending theretrieval request to a corresponding DST execution unit. The methodcontinues at step 662 where the processing module decodes the taskresponse slices to reproduce one or more task responses. The decodingincludes at least one of aggregating the task response slices toreproduce the one or more task responses when the decode thresholdnumber of task response slices correspond to data of the task responses(e.g., and not error coded data).

The method continues at step 664, when the task responses are favorable,the processing module retrieves at least a decode threshold number ofpartial result slices of one or more partial result slice groupings. Theretrieving includes one or more of generating a retrieval request forslices that are of contiguous bytes of a partial result and sending theretrieval request to a corresponding DST execution unit. The methodcontinues at step 666 where the processing module decodes the partialresults slices to reproduce one or more partial results. The decodingincludes at least one of aggregating the partial results slices toreproduce the one or more partial results when the decode thresholdnumber of partial results slices correspond to data of the partialresults (e.g., and not error coded data). The method continues at step668 where the processing module processes the one or more partialresults to produce a result. For example, the processing moduleaggregates the partial results to produce the result.

FIG. 48B is a flowchart illustrating an example of initiating a task,which includes similar steps to FIG. 40B. The method begins at step 670where a processing module (e.g., of a distributed storage and task (DST)execution unit) receives a slice grouping of contiguous data and acorresponding task request slice grouping (e.g., from a DST clientmodule). The method continues at step 672 where the processing modulestores the slice grouping and the task request slice grouping (e.g., inaccordance with a received storage task). For example, the processingmodule stores the slice grouping and the task request slice grouping ina local memory (e.g., as a mailbox).

The method continues at step 674 where the processing module retrievesthe task request slice grouping to reproduce a partial task requestmessage that includes at least one partial task (e.g., retrieving a mailmessage from the mailbox). The method continues with steps 364, 366, and370 of FIG. 40B where the processing module determines execution stepsand schedule, identifies a portion of the contiguous data, and executesthe steps in accordance with the schedule on the portion of thecontiguous data to produce a partial result. The method continues atstep 676 where the processing module generates a partial task responsethat includes a partial result status indicator based on the partialresult. The partial result status indicator includes one of a resultready status level and a result not ready status level.

The method continues at step 678 where the processing module processesthe partial task response in accordance with the processing parametersto produce a task response slice grouping. The processing module mayutilize zero information gain (ZIG) partial slice encoding to generateerror coded task response slices based on one or more of a number ofparticipating pillars, the task response slice grouping, a previous taskresponse slice grouping, an encoding matrix, an error coded data pillarnumber, and a zero information gain slice building approach. The zeroinformation gain slice rebuilding approach is discussed in greaterdetail with reference to FIG. 50. The method continues at step 680 wherethe processing module facilitates storing the task response slicegrouping as task response slices. For example, the processing modulesends the task response slice groupings to a distributed storage andtask network (DSTN) for storage therein (e.g., a return mailbox).

The method continues at step 682 where the processing module processesthe partial result in accordance with the processing parameters toproduce a partial result slice grouping. In addition, the processingmodule may utilize zero information gain (ZIG) partial slice encoding togenerate error coded partial result slices based on one or more of anumber of participating pillars, the partial result slice grouping, aprevious partial result slice grouping, an encoding matrix, an errorcoded data pillar number, and a zero information gain slice buildingapproach. The method continues at step 684 where the processing modulefacilitates storing the partial result slice grouping as partial resultsslices. For example, the processing module sends the partial resultsslice groupings to the distributed storage and task network (DSTN) forstorage therein (e.g., a return mailbox).

FIG. 49 is a flowchart illustrating another example of ingesting data,which includes similar steps to FIG. 5. The method begins at step 686where a processing module (e.g., of a distributed storage and task (DST)execution unit) receives raw data for storage in a distributed storageand task network (DSTN). For example, the processing module receives aweb upload as the raw data. As another example, the processing modulereceives a mass storage upload as the raw data. The method continues atstep 688 where the processing module determines a storage profile forthe raw data. The storage profile includes one or more of a securityrequirement, a performance requirement, an estimated retrievalfrequency, an estimated distributed processing level, a data visibilityprofile, a data owner, a data index storage indicator, and a datadeletion policy. The data deletion policy includes a data deletionindicator specifying circumstances to delete the raw data from the DSTN.For example, the data deletion indicator includes at least one of deleteafter a deletion time period expires, a deletion time period, neverdelete, and delete only upon request.

The method continues at step 690 where the processing module indexes theraw data in accordance with index generation task information to producea data index that includes the storage profile. The method continues atstep 692 where the processing module selects at least one of the rawdata and the data index as data for storage in accordance with thestorage profile. For example, the processing module selects the dataindex for storage in the DSTN and the raw data for storage in a localmemory. The processing module may update a directory to indicate wherethe data is stored.

The method continues at step 694 where the processing module determinesa task corresponding to the data, or that the task includes at least oneof storing the data and processing the data to resize the data includinggenerating indexed data. The determining may be based on one or more ofthe storage profile, a request from a requesting entity, a raw data sizeindicator, a maximum data size threshold, and an execution resourceavailability indicator. The method continues with steps 130-138 of FIG.5 where the processing module determines processing parameters of thedata based on a number of DST execution units, determines taskpartitioning based on the DST execution units and the processingparameters, processes the data in accordance with the processingparameters to produce slice groupings, partitions the task based on thetask partitioning to produce partial tasks, and sends the slicegroupings and corresponding partial tasks to the DST execution units. ADST execution unit subsequently processes a corresponding slice groupingincluding at least one of storing the corresponding slice grouping,processing some of the slice grouping in accordance with the task, anddeleting some of the slice grouping in accordance with at least one ofthe storage profile and the task.

FIG. 50 is a flowchart illustrating an example of modifying a slicegrouping, which includes similar steps to FIGS. 40B and 47B. The methodbegins with step 354 of FIG. 40B where a processing module (e.g., of adistributed storage and task (DST) execution unit) receives at least onepartial task with regards to a group of slices of contiguous data. Themethod continues with step 638 of FIG. 47B where the processing modulereceives the group of slices and continues with steps 364, 366, and 370of FIG. 40B where the processing module determines execution steps andschedule, identifies a portion of the contiguous data, and executes thesteps in accordance with the schedule on the portion of the contiguousdata to produce a partial result.

The method continues at step 696 where the processing module modifies asecond portion of the contiguous data based on the partial result toproduce an updated contiguous data. For example, the processing modulereplaces part of the portion of the contiguous data with the secondportion of the contiguous data when the at least one partial taskspecifies to directly update a portion of the data as the partialresult. The method continues at step 698 where the processing modulestores the updated contiguous data as an updated group of slices (e.g.,stored in a local memory).

The method continues at step 700 where the processing module, for eachgroup of error coded data slices, generates error coded data slicegrouping modification information based on the group of slices and theupdated group of slices. The processing module utilizes a zeroinformation gain (ZIG) slice building approach based on one or more of anumber of participating pillars, the group of slices, the updated groupof slices, an encoding matrix, and an error coded data pillar number.The processing module generates the error coded data slice groupingmodification information as an exclusive OR function (XOR) of a partialencoding of a group of error coded data slices with respect to theupdated group of slices XOR'd with a partial encoding of a group oferror coded data slices with respect to the group of slices.

The processing module generates the partial encoding of the group oferror coded data slices with respect to the updated group of slices byobtaining an encoding matrix utilized to generate the group of errorcoded data slices (e.g., extract from a request, retrieve from amemory), reducing the encoding matrix to produce a square matrix thatexclusively includes rows identified as participating pillars (e.g.,slice pillars associated with participating DST execution units of adecode threshold number of DST execution units), inverting the squarematrix to produce an inverted matrix (e.g., alternatively, may extractthe inverted matrix from the request), matrix multiplying the invertedmatrix by the group of slices to produce a vector, and matrixmultiplying the vector by a row of the encoding matrix corresponding tothe error coded data slice grouping (e.g., alternatively, may extractthe row from the request), to produce the partial encoded group of errorcoded data slices. The processing module generates the partial encodingof the updated group of error coded data slices in a similar fashion byutilizing the updated group of error coded data slices and place of thegroup of error coded data slices.

The method continues at step 702 where the processing module, for eachDST execution unit corresponding to each group of error coded dataslices, sends the error coded data slice grouping modificationinformation to the DST execution unit. The method continues at step 704where the processing module updates a directory to indicate a subsequentrevision is visible. A DST execution unit responsible for error codeddata slices generates and stores updated parity, where the generatingincludes calculating an updated error coded data slice grouping as anXOR of error coded data slice grouping (e.g., previously stored) withreceived error coded data slice grouping modification information.

FIG. 51 is a flowchart illustrating an example of further processing ofa group of slices, which includes similar steps to FIGS. 5, 40B, and47B. The method begins with step 354 of FIG. 40B where a processingmodule (e.g., of a distributed storage and task (DST) execution unit)receives at least one partial task with regards to a group of slices ofcontiguous data and continues with step 638 of FIG. 47B where theprocessing module receives the group of slices. The method continueswith steps 364, 366, and 370 of FIG. 40B where the processing moduledetermines execution steps and schedule, identifies a portion of thecontiguous data, and executes the steps in accordance with the scheduleon the portion of the contiguous data to produce a partial result.

The method continues at step 706 where the processing module determineswhether to further process the partial result. The determining may bebased on one or more of comparing the partial result to a partial resultthreshold with regards to one or more aspects of the partial result. Forexample, the processing module determines to further process the partialresult when the partial result does not include a keyword search aspect.The method branches to step 708 when the processing module determines tofurther process the partial result. The method continues to step 712when the processing module determines to not further process the partialresult. The method continues at step 712, which includes steps 372 and374 of FIG. 40B, where the processing module dispersed storage errorencodes the partial result to produce a plurality of sets of slices andfacilitates storing the plurality of sets of slices in a distributedstorage and task network (DSTN).

The method continues at step 708 where the processing module selects oneor more of the partial result, the contiguous data, and one or moreother contiguous data as data. The selecting may be based on the partialresult, a partial result threshold, a comparison of the partial resultto the partial result threshold, an aspect of the partial result, atrigger associated with the aspect of the partial result, and acomparison of the aspect of the partial result with the triggerassociated with the aspect of the partial result. The selecting enablesprocessing, including at least one of processing the partial resultfurther with a current task, processing the contiguous data with newtasks, and activating additional DST execution units to reprocesscorresponding grouping of slices with new tasks. The method continues atstep 710 where the processing module determines a task corresponding tothe data (e.g., a lookup). The method continues with steps 130-132 ofFIG. 5 where the processing module determines processing parameters ofthe data based on a number of DST execution units and determines taskpartitioning based on the DST execution units and the processingparameters.

The method continues with step 134 of FIG. 5 where the processing moduleprocesses the data in accordance with the processing parameters toproduce slice groupings to align each slice grouping with acorresponding DST execution unit. The method continues with step 136 ofFIG. 5 where the processing module partitions the task based on the taskpartitioning to produce partial tasks. The method continues with step138 of FIG. 5 where the processing module sends the slice groupings ofthe corresponding partial tasks to the DST execution units. For example,the processing module sends slice groupings when sending the partialresult and/or the contiguous data.

FIG. 52 is a flowchart illustrating an example of identifying dataassociations. The method begins at step 714 where a processing module(e.g., of a distributed storage and task (DST) execution unit) obtainsraw data in accordance with the data ingestion task. For example, theprocessing module retrieves locally stored data as the raw data. Themethod continues at step 716 where the processing module indexes the rawdata in accordance with index generation task information to produce adata index. The method continues at step 718 where the processing moduleprocesses the raw data based on the data index and in accordance withdata indexing task information to produce indexed data.

The method continues at step 720 where the processing module facilitatesstoring one or more of the raw data, the data index, and the indexeddata in a distributed storage and task network (DSTN). The storingincludes one or more of selecting the data based on a selection inputand storing the data in accordance with a storage method associated withthe data. For example, the processing module stores the data asdispersed data to facilitate subsequent retrieval. As another example,the processing module stores the data as groups of slices of contiguousdata to facilitate subsequent distributed computing tasks.

The method continues at step 722 where the processing module generatessecond index generation task information based on the data index, theindexed data, and/or an association guideline. The method continues atstep 724 where the processing module indexes the data index inaccordance with the second index generation task information to producea second data index. For example, the processing module searches the rawdata utilizing search parameters of the second index generation taskinformation to produce the second data index.

The method continues at step 726 where the processing module generatessecond data indexing task information based on the second data index.The generating may be based on one or more of the second data index, theindexed data, and the association guideline. The method continues atstep 728 where the processing module processes the raw data based on thesecond data index in accordance with the second data indexing taskinformation to produce second indexed data. For example, the processingmodule extracts portions of the raw data that are associated with thesecond data index and that are relevant with respect to the second dataindexing task information.

The method continues at step 730 where the processing module identifiesone or more associations of data within the raw data based on one ormore of the data index, the second data index, the indexed data, and thesecond indexed data. The identifying may be in accordance with acorrelation guidance information, wherein the correlation guidanceinformation includes data of the second indexed data associated withdata of the data index.

The method continues at step 732 where the processing module facilitatesstoring one or more of the associations, the second data index, and thesecond indexed data in the DSTN. The storing includes one or more ofselecting the one or more of the associations, the second data index,and the second indexed data as association result data and storing thedata in accordance with a storage method associated with the associationresult data. For example, the processing module stores the data asdispersed data to facilitate subsequent retrieval. As another example,the processing module stores the data as groups of slices of contiguousdata to facilitate subsequent distributed computing tasks.

FIG. 53A is a diagram illustrating encoding of data 734 that includesdata 734 organized as a plurality of chunksets 1-N (e.g., a datapartition, or portion thereof), a chunkset data matrix 736 for each ofthe plurality of chunksets 1-N that includes a row for each chunk, agenerator matrix 738 to encode each chunkset column by column via acolumn selector 746 as a data selection 740 to produce a correspondingchunkset matrix of slices 742, and a pillar selector 744 to route slicesof each chunkset to a corresponding distributed storage and taskexecution (DST EX) unit for task processing.

A number of chunks per chunkset is determined as a number of requiredparallel DST execution units to process parallel task processing tocomplete an overall task within a desired task execution time period. Adecode threshold of an information dispersal algorithm (IDA) isdetermined as the number of chunks. A pillar width number of the IDA isdetermined based on or more of the decode threshold, a number ofavailable DST EX units, an availability requirement, and a reliabilityrequirement. For example, the decode threshold is set at 5 when thenumber of chunks is 5 and the pillar width is set at 8 in accordancewith a reliability requirement.

A chunk size of each chunkset is determined to match a chunk sizerequirement for task processing. For example, a chunk size is determinedas 20 k bytes when a DST EX unit indicates that a task processing datasize limit is 20 k bytes. A chunkset size is the number of chunksmultiplied by the chunk size. For example, the chunkset is 100 k byteswhen the chunk size is 20 k bytes and the number of chunks is 5. Anumber of chunksets N is determined as a size of the data divided by thesize of the chunkset.

The generator matrix 738 is determined in accordance with the IDA andincludes a decode threshold number of columns and a pillar width numberof rows. A unity matrix is utilized as a top square matrix to facilitategeneration of contiguous slices that match contiguous data of chunks.Other rows of the encoding matrix facilitate generating error codedslices for remaining rows of the chunkset slice matrix.

For each chunkset, the generator matrix 738 is matrix multiplied by acolumn of the corresponding chunkset data matrix 736 (e.g., dataselection 740 as selected by column selector 746) to generate a columnof the chunkset slice matrix 742 for the corresponding chunkset. Forexample, row 1 of the generator matrix 738 is matrix multiplied bycolumn 1 of the chunkset data matrix 736 to produce a row 1 byte ofcolumn 1 of the chunkset slice matrix 742, row 2 of the generator matrix738 is matrix multiplied by column 1 of the chunkset data matrix 736 toproduce a row 2 byte of column 1 of the chunkset slice matrix 742, etc.As another example, row 1 of the generator matrix 738 is matrixmultiplied by column 2 of the chunkset data matrix to produce a row 1byte of column 2 of the chunkset slice matrix, row 2 of the generatormatrix is matrix multiplied by column 2 of the chunkset data matrix 736to produce a row 2 byte of column 2 of the chunkset slice matrix 742,etc.

A segment may be considered as one or more columns of the chunkset datamatrix 736 and slices that correspond to the segment are the rows of thechunkset slice matrix 742 that correspond to the one or more columns ofthe chunkset data matrix 736. For example, row 1 columns 1 and 2 of thechunkset slice matrix 742 form slice 1 when columns 1 and 2 of thechunkset data matrix 736 are considered as a corresponding segment.Slices of a common row of the chunkset slice matrix 742 are of a chunkof contiguous data of the data 734 and share a common pillar number andshall be stored in a common DST EX unit to facilitate a distributedtask.

The pillar selector 744 routes slices of each pillar to a DST EX unit inaccordance with a pillar selection scheme. For example, two slices ofrow 1 (e.g., slice comprising bytes from columns 1 through 10k and slice2 comprising bytes from columns 10k+1 through 20k) of the chunkset slicematrix 742 are sent to DST EX unit 1 as a contiguous chunk of data thatincludes 20 k bytes when the pillar selection scheme maps pillars 1-5(e.g., associated with slices of contiguous data), to DST EX units 1-5and maps pillars 6-8 (e.g., associated with error coded slices) to DSTEX units 6-8 for a first chunkset.

To facilitate load leveling of tasks executed by the DST EX units, thepillar selection scheme may include rotating assignments of pillars todifferent DST EX units for each chunkset. For example, two slices of row8 (e.g., slice comprising bytes from columns 1 through 10k and slice 2comprising bytes from columns 10k+1 through 20k) of the chunkset slicematrix 742 are sent to DST EX unit 1 as error coded data slices thatincludes 20 k bytes when the pillar selection scheme maps pillar 8(e.g., associated with error coded slices), to DST EX units 1 and mapspillars 1 (e.g., associated with slices of contiguous data) to DST EXunits 8 for another chunkset.

FIG. 53B is a flowchart illustrating an example of generating a slicegrouping, which includes similar steps to FIG. 5. The method begins withstep 126 of FIG. 5 where a processing module (e.g., of a distributedstorage and task (DST) client module) receives data and a correspondingtask. The method continues at step 748 where the processing moduleselects a number of DST execution units to favorably execute partialtasks of the corresponding tasks. The selecting includes determining anumber of simultaneous compute resources to complete the task in afavorable timeframe based on DST execution unit capability. The methodcontinues at step 750 where the processing module determines taskpartitioning based on one or more of distributed computing capabilitiesof the selected DST execution units. The determining includes at leastone of aligning task partitions with DST execution unit capabilities andaligning subsequent computing tasks (e.g., based on partial results)with DST execution unit capabilities.

The method continues at step 752 where the processing module determinesprocessing parameters of the data based on the task partitioning. Thedetermining includes determining partitioning of data into chunks andchunksets based on the number of DST EX units to favorably execute thepartial tasks. The method continues with steps 136 and 134 of FIG. 5where the processing module partitions the tasks based on the taskpartitioning to produce partial tasks and processes the data inaccordance with the processing parameters to produce slice groupings.The method continues at step 754 where the processing module sends theslice groupings in the corresponding partial tasks to the DST executionunits in accordance with the pillar mapping. The processing module mayobtain the pillar mapping based on one or more of receiving the mapping,a query, and generating the mapping based on a data processing loadleveling requirement. The pillar mapping may include rotation ofassignment of slice groupings by pillar to different DST execution units(e.g., a round-robin approach to facilitate load leveling).

FIG. 54 is a flow chart illustrating an example of selecting distributedcomputing resources, which includes similar steps to FIGS. 5 and 53B.The method begins with step 126 of FIG. 5 where a processing module(e.g., of a distributed storage and task (DST) client module) receivesdata and a corresponding task. The method continues at step 756 wherethe processing module identifies candidate DST execution units forexecuting partial tasks of the corresponding task. The identifying mayinclude obtaining a distributed task computing capability level by oneor more of a query, a lookup, and receiving a message and selecting thecandidate DST execution units associated with favorable distributed taskcomputing capability levels (e.g., above a threshold). A distributedtask computing capability level includes one or more of a processingcapability level, a memory capacity level, a network access level, abandwidth capability level, an availability level, and a reliabilitylevel.

The method continues at step 758 where the processing module obtainsdistributed computing capabilities of the candidate DST execution unitsbased on one or more of a query, a lookup, and receiving a message. Themethod continues at step 760 where the processing module selects anumber of DST execution units of the candidate DST execution units tofavorably execute the partial tasks of the corresponding tasks. Theselecting includes identifying a number of simultaneous computeresources to execute the task in a favorable timeframe based on thedistributed computing capabilities of the candidate DST execution units.

The method continues at step 762 where the processing module determinestask partitioning based on one or more of the distributed computingcapabilities of the selected DST execution units, the processingparameters, and an estimated next data processing destination. Thedetermining includes aligning tasks with DST capabilities for currentand potential future tasks. The method continues with step 752 of FIG.53B where the processing module determines processing parameters of thedata based on the task partitioning and continues with steps 136, 134,and 138 of FIG. 5 where the processing module partitions the tasks basedon the task partitioning to produce partial tasks, processes the data inaccordance with the processing parameters to produce slice groupings,and sends the slice groupings and corresponding partial tasks to the DSTexecution units.

FIG. 55 is a flowchart illustrating an example of retrieving distributedcomputed data, which includes similar steps to FIG. 48A. The methodbegins at step 764 where a processing module (e.g., of a distributedstorage and task (DST) client module) receives a retrieve data request(e.g., from a user device), where the data is stored in a distributedstorage and task network (DSTN) as a result of at least one executedpartial task. For example, the data represents a partial result of apreviously executed task. As another example, the data represents modifydata of the previously executed task. The method continues at step 766where the processing module identifies DST execution units of the DSTNassociated with the data. The association includes one or more of whereinitial data was sent for storage and/or processing of a task, where apartial task was sent for storage and/or processing, where a partialresult (e.g., an intermediate result) was sent for storage and/orfurther processing, where a subsequent partial task was sent for storageand/or processing, a location received in response to a query of DSTallocation information, and a location extracted from a location tablelookup. The method continues with steps 660 and 662 of FIG. 48A wherethe processing module retrieves at least a decode threshold number oftask response slices of one or more task response slice groupings fromthe DST execution units and decodes the task response slices toreproduce one or more task responses.

The method continues at step 768 where the processing module determinessecond DST execution units associated with partial results based on theone or more task responses. The determining may be based on one or moreof extracting second DST execution unit identifier information from theone or more task responses and performing a lookup to extract the secondDST execution unit identifiers. For example, the processing moduleaccesses DST allocation information to retrieve identifiers of thesecond DST execution units.

The method continues at step 770 where the processing module retrievesat least a decode threshold number of partial results slices of one ormore partial results slice groupings from the second DST executionunits. The retrieving includes one or more of selecting DST executionunits associated with the one or more partial results slice groupings(e.g., that contain results and not error coded data), generating sliceretrieval requests, sending the slice retrieval requests to identify DSTexecution units, and receiving the at least the decode threshold numberof partial results slices. The method continues with steps 666 and 668of FIG. 48A where the processing module decodes the partial resultsslices to reproduce one more partial results and processes the one ormore partial results to produce a result.

FIG. 56 is a flowchart illustrating an example of load-balancingdistributed computing resources, which includes similar steps to FIGS.5, 53B, and 54. The method begins with step 126 of FIG. 5 where aprocessing module (e.g., of a distributed storage and task (DST) clientmodule) receives data and a corresponding task and continues with steps756, 758, 760, and 762 of FIG. 54 where the processing module identifiescandidate DST execution units for executing partial tasks of thecorresponding task, obtains distributed computing capabilities of thecandidate DST execution units, selects a number of DST execution unitsof the candidate DST execution units to favorably execute the partialtasks of the corresponding task, and determines task partitioning basedon one or more of the distributed computing capabilities of the selectedDST execution units, the processing parameters, and an estimated nextdata processing destination. The method continues with step 752 of FIG.53B where the processing module determines processing parameters of thedata based on the task partitioning and continues with steps 136 and 134of FIG. 5 where the processing module partitions the task based on thetask partitioning to produce partial tasks and processes the data inaccordance with the processing parameters to produce slice groupings.

The method continues at step 772 where the processing module determinesa pillar mapping for at least some of the slice groupings. Thedetermining includes identifying a favorable assignment of DST executionresources to tasks based on current information with regards to the DSTexecution resources and requirements of task execution. For example, theprocessing module may determine a round robin pillar mapping approach toevenly load a decode threshold number of the DST execution units. Themethod continues at step 774 where the processing module sends at leastsome of the slice groupings and corresponding partial tasks to the DSTexecution units in accordance with the pillar mapping.

The method continues at step 776 where the processing module obtains DSTexecution unit status information with regards to executing the partialtasks. The obtaining includes at least one of initiating a query,receiving status information, and performing a lookup to extract statusinformation. The method continues at step 778 where the processingmodule updates the pillar mapping based on the DST execution unit statusinformation. For example, the processing module determines an updatedpillar mapping to shift DST execution resource loading from the busiestresources to resources that have more favorable available task executioncapacity. The method continues at step 780 where the processing modulesends other slice groupings and corresponding partial tasks to the DSTexecution units in accordance with the updated pillar mapping. Forexample, the processing module sends the other slice groupings andcorresponding partial tasks to DST execution units to execute successivesteps utilizing improved task execution capability. The process maycontinue to adjust the pillar mapping until all the partial tasks havebeen executed corresponding to the task.

FIG. 57 is a flowchart illustrating an example of transforming a taskinto sub-tasks, which includes similar steps to FIGS. 5, 40B, and 47B.The method begins with step 354 of FIG. 40B where a processing module(e.g., of a distributed storage and task (DST) execution unit) receivesat least one partial task with regards to a group of slices ofcontiguous data and continues with step 638 of FIG. 47B where theprocessing module receives the group of slices. The method continues atstep 782 where the processing module determines whether to process theat least one partial task locally. The determining may be based on oneor more of a local task execution capacity level, a required taskexecution capacity level (e.g., to execute the partial task within arequired task execution timeframe), and a comparison of the differenceof the local task execution capacity level to the required taskexecution capacity level to a difference threshold. For example, theprocessing module determines to process the at least one partial tasklocally when the difference compares favorably to the differencethreshold (e.g., local task execution meets the required task executiontimeframe).

The method branches to step 784 when the processing module determinesnot to process the at least one partial task locally. The methodcontinues to step 364 of FIG. 40B when the processing module determinesto process the at least one partial task locally. The method continueswith steps 364, 366, and 370 of FIG. 40B where the processing moduledetermines execution steps and schedule, identifies a portion of thecontiguous data, and executes the steps in accordance with the scheduleon the portion of the contiguous data to produce a partial result.

The method continues at step 784 where the processing module selects aportion of the contiguous data as data when the processing moduledetermines not to process the at least one partial task locally. Theselecting includes determining which portion to process locally andwhich portions to process with other DST execution units based on one ormore of DST execution unit task execution capacity and the required taskexecution timeframe such that the partial task is executed within therequired timeframe. The method continues with step 130 of FIG. 5 wherethe processing module determines processing parameters of the data basedon a number of DST execution units.

The method continues at step 786 where the processing module determinestask partitioning based on the DST execution units and the processingparameters to transform the at least one partial task into at least onesecondary partial task. For example, the processing module determinespartitioning to form one or more sub-tasks as the at least one secondarypartial tasks for execution by the number of DST execution units. Themethod continues at step 788 where the processing module processes thedata in accordance with the processing parameters to produce secondaryslice groupings. For example, the processing module generates groups ofslices in accordance with the processing parameters to produce thesecondary slice groupings.

The method continues at step 790 where the processing module sends thesecondary slice groupings and corresponding secondary partial tasks tothe DST execution units. The method continues at step 792 where theprocessing module receives one or more secondary partial results (e.g.,from the DST execution units). The method continues at step 794 wherethe processing module processes the one or more secondary partialresults to produce a partial result. The processing includes at leastone of decoding and/or aggregating. In addition, the processing modulemay send the partial result to a requesting entity and/or facilitatestoring of the partial result in a distributed storage and task network(DSTN).

FIG. 58A is a diagram of another example of error encoding and slicingprocessing of dispersed error encoding to facilitate storing data inaccordance with a computational-orientated dispersed storage errorcoding function (e.g., to enable execution of a portion of a task on thestored data). In this example, data segment 1 includes 3 rows with eachrow being treated as one word for encoding. As such, data segment 1includes three words for encoding: word 1 including data blocks d1 andd2, word 2 including data blocks d16 and d17, and word 3 including datablocks d31 and d32. Each of data segments 2-7 includes three words whereeach word includes two data blocks. Data segment 8 includes three wordswhere each word includes a single data block (e.g., d15, d30, and d45).

Each data segment is converted via an error encoding and slicing 796into a set of encoded data slices in accordance with error correctionencoding parameters. More specifically, when the error correctionencoding parameters indicate a unity matrix Reed-Solomon based encodingalgorithm, 5 pillars, and decode threshold of 3, the first three encodeddata slices of the set of encoded data slices for a data segment aresubstantially similar to the corresponding word of the data segment. Forinstance, when the unity matrix Reed-Solomon based encoding algorithm isapplied to data segment 1, the content of the first encoded data slice(DS1_d1&2) of the first set of encoded data slices (e.g., correspondingto data segment 1) is substantially similar to content of the first word(e.g., d1 & d2); the content of the second encoded data slice(DS1_d16&17) of the first set of encoded data slices is substantiallysimilar to content of the second word (e.g., d16 & d17); and the contentof the third encoded data slice (DS1_d31&32) of the first set of encodeddata slices is substantially similar to content of the third word (e.g.,d31 & d32).

The content of the fourth and fifth encoded data slices (e.g., ES1_1 andES1_2) of the first set of encoded data slices include error correctiondata based on the first-third words of the first data segment. With suchan encoding and slicing scheme, retrieving any three of the five encodeddata slices allows the data segment to be accurately reconstructed.

The encoding and slice slicing of data segments 2-7 yield sets ofencoded data slices similar to the set of encoded data slices of datasegment 1. For instance, the content of the first encoded data slice(DS2_d3&4) of the second set of encoded data slices (e.g., correspondingto data segment 2) is substantially similar to content of the first word(e.g., d3 & d4); the content of the second encoded data slice(DS2_d18&19) of the second set of encoded data slices is substantiallysimilar to content of the second word (e.g., d18 & d19); and the contentof the third encoded data slice (DS2_d33&34) of the second set ofencoded data slices is substantially similar to content of the thirdword (e.g., d33 & d34). The content of the fourth and fifth encoded dataslices (e.g., ES1_1 and ES1_2) of second the second set of encoded dataslices include error correction data based on the first-third words ofthe second data segment.

The sets of encoded data slices are utilized to form slice groupings fora set of distributed storage and task (DST) execution units. Slicegrouping selection processing is performed in accordance with groupselection information. In this example, the sets of encoded data slicesare organized into five slice groupings (one for each DST execution unitof a set of DST execution units). As a specific example, the groupingselection module creates a first slice grouping for DST execution unit#1, which includes the first encoded slices of each of the sets ofencoded slices. As such, the first DST execution unit receives encodeddata slices corresponding to data blocks 1-15 (e.g., encoded data slicesof contiguous data to enable execution of the portion of the task on thestored data).

The grouping selection module also creates a second slice grouping forDST execution unit #2, which includes the second encoded slices of eachof the sets of encoded slices. As such, the second DST execution unitreceives encoded data slices corresponding to data blocks 16-30. Thegrouping selection module further creates a third slice grouping for DSTexecution unit #3, which includes the third encoded slices of each ofthe sets of encoded slices. As such, the third DST execution unitreceives encoded data slices corresponding to data blocks 31-45.

The grouping selection module creates a fourth slice grouping for DSTexecution unit #4, which includes the fourth encoded slices of each ofthe sets of encoded slices. As such, the fourth DST execution unitreceives encoded data slices corresponding to first error encodinginformation (e.g., encoded data slices of error coding (EC) data). Thegrouping selection module further creates a fifth slice grouping for DSTexecution unit #5, which includes the fifth encoded slices of each ofthe sets of encoded slices. As such, the fifth DST execution unitreceives encoded data slices corresponding to second error encodinginformation.

FIG. 58B is a diagram of an example of transforming data blocks fromstored data that was stored in accordance with acomputational-orientated dispersed storage error coding function to datastored in accordance with a long-term-storage-orientated dispersedstorage error coding function. Slice groupings including data blocksstored in accordance with the computational-orientated dispersed storageerror coding function stored in a set of distributed storage and task(DST) execution units are transformed using a transform function 798 toproduce sets of data blocks stored in accordance with thelong-term-storage-orientated dispersed storage error coding function.Utilization of the long-term-storage-orientated dispersed storage errorcoding function results in contiguous data blocks arranged by a set ofencoded data slices. The transform function 798 produces a mapping ofslices and facilitates storage of the slices in accordance with themapping. As a specific example, a first set of encoded data slicesincludes three encoded data slices from DST execution unit 1, includinga first encoded data slice that includes data blocks 1&2 (DS1_d1&2), asecond encoded data slice that includes data blocks 3&4 (DS1_d3&4), anda third encoded data slice that includes data blocks 5&6 (DS1_d5&6). Thetransform function 798 results in the first encoded data slice remainingstored in DST execution unit 1, transfer of the second encoded dataslice from DST execution unit 1 to DST execution unit 2, and transfer ofthe third encoded data slice from DST execution unit 1 to DST executionunit 3.

Each set of encoded data slices includes one or more encoded data slicescorresponding to error encoding information (e.g., encoded data slicesof error coding (EC) data). The transform function 798 results inencoding of a decode threshold number of encoded data slices of each setof encoded data slices to produce the one or more encoded data slicescorresponding to error coding information. For example, the firstencoded data slice, the second encoded data slice, and the third encodeddata slice are dispersed storage error and encoded to produce twotransformed error slices ES T1_1 and ES T1_2 when a decode threshold is3 and a pillar with is 5. Transformed error slice ES T1_1 is stored atDST execution unit 4 and transformed error slice ES T1_2 is stored atDST execution unit 5.

A de-slicing and error decoding function 800 is utilized to decode thesets of encoded data slices stored in accordance with thelong-term-storage-orientated dispersed storage error coding function toproduce a plurality of data segments in accordance with long-termstorage. As such, each data segment represents words of one or more datablocks of a contiguous data portion of the data. For example, at least adecode threshold number of encoded data slices of the first set ofencoded data slices (e.g., that includes the first encoded data slice,the second encoded data slice, and the third encoded data slice) isdispersed storage error decoded in accordance with the de-slicing anderror decoding function 800 to produce a data segment including datablocks d1-d6.

FIG. 58C is a schematic block diagram of another embodiment of adistributed computing system that includes a computing device 802 and aset of distributed storage and task (DST) units 804. The set of DSTunits 804 includes one or more DST units 806. A DST unit 806 of the setof DST units 804 may be implemented by one or more of a DST executionunit, a server, the user device, and a DST processing unit. Thecomputing device 802 may be implemented by one or more of a DST unit 806of the set of DST units 804, a DST execution unit, a DST client module,a distributed task (DT) execution module, a processing module, acontroller, a user device, a DST processing unit, a distributed storageand task network (DSTN) managing unit, and a DST integrity processingunit. The computing device 802 includes a distributed storage (DS)module 808. The DS module 808 includes a determine module 810, atransform module 812, and an obtain module 814.

The system is operable to long-term store at least a portion oftemporarily stored data in the set of DST units 804. The temporarilystored data is stored in the set of DST units 804 in accordance with acomputational-orientated dispersed storage error coding function. Thecomputational-orientated dispersed storage error coding functionincludes first dispersed storage error coding parameters that enables aDST unit 806 of the set of DST units 804 to recover, in thepre-dispersed storage error encoded format, a sub-portion of the portionof the temporarily stored data from encoded data slices (e.g.,computational slices 816, data and/or check blocks) the DST unit 806stores (e.g., with minimal communication with other DST units 806 of theset of DST units 804).

The determine module 810 determines whether the at least a portion oftemporarily stored data is to be stored long-term. The determining maybe based on at least one of receiving a message, detecting expiration ofa temporarily stored data time period, and determining that no furthercomputations are to be performed on the at least a portion of thetemporarily stored data. For example, the DS module 808 performs aseries of computational tasks and upon completion the determine module810 indicates that the at least a portion of the temporarily stored datais to be stored long-term.

When the at least a portion of the temporarily stored data is to bestored long-term, the transform module 812 performs a series of steps totransform the temporarily stored data to long-term stored data. In afirst step of the transforming, the transform module 812 identifies oneor more DST units 806 of the set of DST units 804 storing the at least aportion of the temporarily stored data in accordance with thecomputational-orientated dispersed storage error coding function. Thetransform module 812 identifies the one or more DST units 806 of the setof DST units 804 by at least one of a plurality of approaches. A firstapproach includes receiving a message regarding the at least a portionof the temporarily stored data. For example, the message identifies theone or more DST units 806. A second approach includes receiving amessage regarding the at least a portion of the temporarily stored dataand determining the one or more DST units 806 based on storage of the atleast a portion of the temporarily stored data. A third approachincludes determining that no further computations are to be performed onthe at least a portion of the temporarily stored data.

In a second step of the transforming, the transform module 812 recoversthe at least a portion of the temporarily stored data from the one ormore DST units 806 in a pre-dispersed storage error encoded format(e.g., data still includes pre-data manipulation functions such asencryption). The recovering includes at least one of retrieving andfacilitating transfer (e.g., outputting a request to transfer). Forexample, the transform module 812 retrieves the computational slices 816of the temporarily stored data from the one or more DST units 806.

In a third step of the transforming, the transform module 812 dispersedstorage error encodes the at least a portion of the temporarily storeddata in the pre-dispersed storage error encoded format (e.g.,computational slices 816) into a plurality of sets of encoded dataslices (e.g., storage slices 818) in accordance with along-term-storage-orientated dispersed storage error coding function.The long-term-storage-orientated dispersed storage error coding functionincludes second dispersed storage error coding parameters that preventsthe DST unit 806 from recovering, in the pre-dispersed storage errorencoded format, the sub-portion of the portion of the temporarily storeddata from encoded data slices the DST unit 806 stores and requiresretrieval of encoded data slices from multiple DST units 806 of the setof DST units 804 to recover, in the pre-dispersed storage error encodedformat, the sub-portion of the portion of the temporarily stored data.

The transform module 812 dispersed storage error encodes the at least aportion of the temporarily stored data by a sequence of procedures. Afirst procedure includes determining, in accordance with thelong-term-storage-orientated dispersed storage error coding function, adata mapping of data-based encoded data slices of the at least a portionof the temporarily stored data in a pre-dispersed storage error encodedformat. A second procedure includes generating redundancy-based encodeddata slices based on the data-based encoded data slices and inaccordance with the long-term-storage-orientated dispersed storage errorcoding function. A third procedure includes organizing the data-basedencoded data slices and the redundancy-based encoded data slices intothe plurality of set of encoded data slices (e.g., storage slices 818).

Alternatively, the transform module 812 dispersed storage error encodesthe at least a portion of the temporarily stored data by a sequence ofalternative procedures. A first alternative procedure includes dispersedstorage error encoding the at least a portion of the temporarily storeddata in the pre-dispersed storage error encoded format into a pluralityof encoded data blocks in accordance with thelong-term-storage-orientated dispersed storage error coding function. Asecond alternative procedure includes organizing the plurality ofencoded data blocks into the plurality of sets of encoded data slices inaccordance with the long-term-storage-orientated dispersed storage errorcoding function. For example, utilizing an on-line code conversionprocess.

In a fourth step of the transforming, the transform module 812 storesthe plurality of sets of encoded data slices (e.g., storage slices 818)in the set of DST units 804. The obtain module 814 functions to obtainthe temporarily stored data by at least one of two obtaining approaches.A first obtaining approach includes a series of first obtaining approachsteps. A first step of the first obtaining approach steps includestemporarily storing raw data 820, as the temporarily stored data, in theset of DST units 804 in accordance with the computational-orientateddispersed storage error coding function. For example, the obtain module814 dispersed storage error encodes the raw data 820 using thecomputational-orientated dispersed storage error coding function inaccordance with the first dispersed storage error coding parameters toproduce a plurality of sets of encoded raw slices 822. Alternatively, atleast some of the DST units 806 obtains the raw data 820 and encodes theraw data 820 to produce the plurality of sets of encoded raw slices 822.Next, the obtain module 814 facilitates storage of the plurality of setsof encoded raw slices 822 in the set of DST units 804.

A second step of the first obtaining approach steps includes performing,by at least some of the DST units 806, a task on the temporarily storeddata to produce found data 824. A third step of the first obtainingapproach steps includes dispersed storage error encoding the found data824, as the at least a portion of the temporarily stored data, into theplurality of sets of encoded data slices in accordance with thelong-term-storage-orientated dispersed storage error coding function(e.g., the computational slices 816). Alternatively, at least some ofthe DST units 806 dispersed storage error encodes the found data 824 toproduce a plurality of sets of encoded data slices in accordance withthe long-term-storage-orientated dispersed storage error codingfunction.

A second obtaining approach includes a series of second obtainingapproach steps. A first step of the second obtaining approach stepsincludes temporarily storing found data 824, as the temporarily storeddata, in the set of DST units 804 in accordance with thecomputational-orientated dispersed storage error coding function, whereat least some of the DST units 806 performed a task on the raw data 820to produce the found data 824. A second step of the second obtainingapproach steps includes performing, by at least some of the DST units806, a second task on the temporarily stored data to produce a sub-setof found data 826. A third step of the second obtaining approach stepsincludes dispersed storage error encoding the sub-set of found data 826,as the at least a portion of the temporarily stored data, into theplurality of sets of encoded data slices in accordance with thelong-term-storage-orientated dispersed storage error coding function.

FIG. 58D is a flowchart illustrating an example of transforming data.The method begins at step 830 where a processing module (e.g., of adistributed storage and task (DST) unit) temporarily stores raw data, astemporarily stored data, in a set of DST units in accordance with acomputational-orientated dispersed storage error coding function. Thecomputational-orientated dispersed storage error coding functionincludes first dispersed storage error coding parameters that enables aDST unit of the set of DST units to recover, in a pre-dispersed storageerror encoded format, a sub-portion of a portion of temporarily storeddata from encoded data slices the DST unit stores. The method continuesat step 832 where at least some of the DST units performs a task on thetemporarily stored data to produce found data.

The method continues at step 834 where the processing module dispersedstorage error encodes the found data, as the at least a portion of thetemporarily stored data, into a plurality of sets of encoded data slicesin accordance with a long-term-storage-orientated dispersed storageerror coding function. The long-term-storage-orientated dispersedstorage error coding function includes second dispersed storage errorcoding parameters that prevents the DST unit from recovering, in thepre-dispersed storage error encoded format, the sub-portion of theportion of the temporarily stored data from encoded data slices the DSTunit stores and requires retrieval of encoded data slices from multipleDST units of the set of DST units to recover, in the pre-dispersedstorage error encoded format, the sub-portion of the portion of thetemporarily stored data.

Alternatively, or in addition to, at least some of the DST units obtainthe raw data. When the at least some of the DST units obtain the rawdata, the method continues at step 836 where the at least some of theDST units performs a task on the raw data to produce the found data. Themethod continues at step 838 where the processing module temporarilystores the found data, as the temporarily stored data, in the set of DSTunits in accordance with the computational-orientated dispersed storageerror coding function. The method continues at step 840 where the atleast some of the DST units performs a second task on the temporarilystored data to produce a sub-set of found data. The method continues atstep 844 where the processing module dispersed storage error encodes thesub-set of found data, as the at least a portion of the temporarilystored data, into the plurality of sets of encoded data slices inaccordance with the long-term-storage-orientated dispersed storage errorcoding function.

The method continues at step 846 where the processing module determineswhether at least a portion of the temporarily stored data is to bestored long-term, where the temporarily stored data is stored in the setof DST units in accordance with the computational-orientated dispersedstorage error coding function. For example, a processing moduledetermines that no further computational tasks are to be performed onthe temporarily stored data.

When the at least a portion of the temporarily stored data is to bestored long-term, the method continues at step 848 where the processingmodule identifies one or more DST units of the set of DST units storingthe at least a portion of the temporarily stored data in accordance withthe computational-orientated dispersed storage error coding function.The identifying the one or more DST units of the set of DST unitsincludes at least one of a plurality of approaches. A first approachincludes receiving a message regarding the at least a portion of thetemporarily stored data, where the message identifies the one or moreDST units. A second approach includes receiving a message regarding theat least a portion of the temporarily stored data and determining theone or more DST units based storage of the at least a portion of thetemporarily stored data. A third approach includes determining that nofurther computations are to be performed on the at least a portion ofthe temporarily stored data.

The method continues at step 850 where the processing module recoversthe at least a portion of the temporarily stored data from the one ormore DST units in the pre-dispersed storage error encoded format. Themethod continues at step 852 where the processing module dispersedstorage error encodes the at least a portion of the temporarily storeddata in the pre-dispersed storage error encoded format into theplurality of sets of encoded data slices in accordance with along-term-storage-orientated dispersed storage error coding function.The dispersed storage error encoding the at least a portion of thetemporarily stored data includes a series of steps. A first stepincludes determining, in accordance with thelong-term-storage-orientated dispersed storage error coding function, adata mapping of data-based encoded data slices of the at least a portionof the temporarily stored data in a pre-dispersed storage error encodedformat. A second step includes generating redundancy-based encoded dataslices (e.g., error coded slices) based on the data-based encoded dataslices and in accordance with the long-term-storage-orientated dispersedstorage error coding function. A third step includes organizing thedata-based encoded data slices and the redundancy-based encoded dataslices into the plurality of set of encoded data slices.

Alternatively, the dispersed storage error encoding the at least aportion of the temporarily stored data includes an alternate series ofsteps. A first alternate step includes dispersed storage error encodingthe at least a portion of the temporarily stored data in thepre-dispersed storage error encoded format into a plurality of encodeddata blocks in accordance with the long-term-storage-orientateddispersed storage error coding function. A second alternate stepincludes organizing the plurality of encoded data blocks into theplurality of sets of encoded data slices in accordance with thelong-term-storage-orientated dispersed storage error coding function.The method continues at step 854 where the processing module stores theplurality of sets of encoded data slices in the set of DST unitssubsequent to the encoding.

FIG. 58E is a schematic block diagram of another embodiment of adistributed computing system that includes a computing device 860 and aset of distributed storage and task (DST) units 804. The set of DSTunits 804 includes one or more DST units 806. A DST unit 806 of the setof DST units 804 may be implemented by one or more of a DST executionunit, a server, the user device, and a DST processing unit. Thecomputing device 860 may be implemented by one or more of a DST unit 806of the set of DST units 804, a DST execution unit, a DST client module,a distributed task (DT) execution module, a processing module, acontroller, a user device, a DST processing unit, a distributed storageand task network (DSTN) managing unit, and a DST integrity processingunit. The computing device 860 includes a distributed storage (DS)module 862. The DS module 862 includes a determine task module 864, areconfigure module 866, and a task module 868.

The system is operable to transform data stored in accordance with along-term-storage-orientated dispersed storage error coding functioninto data stored with a computational-orientated dispersed storage errorcoding function to facilitate performing of a task 870 on a recoveredportion of the data to produce a task resultant. The data is encodedinto a plurality of encoded data blocks 874 (e.g., sets of encodedslices, groups of check blocks, storage blocks 874) in accordance withthe long-term-storage-orientated dispersed storage error coding functionand the plurality of encoded data blocks 874 are stored in the set ofDST units 804. The determine task module 864 determines that the task870 is to be performed on the data. The determining may be based on oneor more of receiving a task execution request, receiving the task 870,identifying a task execution need based on analysis of the data, a taskexecution schedule, and a predetermination.

The reconfigure module 866 reconfigures storage of the data from thelong-term-storage-orientated dispersed storage error coding function tothe computational-orientated dispersed storage error coding function,where the data is encoded into groupings of encoded data blocks 872(e.g., computational data blocks 872) in accordance with thecomputational-orientated dispersed storage error coding function. Thereconfigure module 866 reconfigures storage of the data by a series ofsteps. A first step includes decoding the plurality of encoded datablocks 874 in accordance with the long-term-storage-orientated dispersedstorage error coding function to recover the data. A second stepincludes encoding the recovered data in accordance with thecomputational-orientated dispersed storage error coding function toproduce the groupings of encoded data blocks 872. A third step includessending one of the groupings of encoded data blocks 872 to a DST unit806.

The reconfigure module 866 may reconfigure storage of the data by aseries of alternate steps. A first alternate step includes identifyingdata-based encoded data slices of the plurality of encoded data blocks874 in accordance with the long-term-storage-orientated dispersedstorage error coding function. A second alternate step includesdetermining a data mapping of the data-based encoded data slices betweenthe set of DST units 804 and the at least some of the DST units 806. Athird alternate step includes facilitating copying of at least some ofthe data-based encoded data slices to the at least some of the DST units806 in accordance with the data mapping.

The task module 868 facilitates performing the task 870 by a series ofsteps. A first step includes facilitating storage of the groupings ofencoded data blocks 872 in the at least some of the set of DST units806, where a DST unit 806 of the at least some of the DST units 806recovers a portion of the data from the one of the groupings of encodeddata blocks 872 and performs a portion of the task 870 on the recoveredportion of the data to produce a partial task resultant 876. A secondstep includes receiving partial task resultants 876 from the at leastsome of the set of DST units 806. A third step includes compiling thepartial task resultants 876 to produce the task resultant 878.

FIG. 58F is a flowchart illustrating another example of transformingdata. The method begins at step 880 where a processing module (e.g., ofa distributed storage and task (DST) unit) determines that a task is tobe performed on data, where the data is encoded into a plurality ofencoded data blocks (e.g., sets of encoded slices, groups of checkblocks) in accordance with a long-term-storage-orientated dispersedstorage error coding function and the plurality of encoded data blocksare stored in a set of DST units.

The method continues at step 882 with a processing module reconfiguresstorage of the data from the long-term-storage-orientated dispersedstorage error coding function to a computational-orientated dispersedstorage error coding function, where the data is encoded into groupingsof encoded data blocks in accordance with the computational-orientateddispersed storage error coding function. The reconfiguring storage ofthe data includes a series of steps. A first step includes decoding theplurality of encoded data blocks in accordance with thelong-term-storage-orientated dispersed storage error coding function torecover the data. A second step includes encoding the recovered data inaccordance with the computational-orientated dispersed storage errorcoding function to produce the groupings of encoded data blocks. A thirdstep includes sending one of the groupings of encoded data blocks to aDST unit of the set of DST units. Alternatively, the reconfiguringstorage of the data includes a series of alternate steps. A firstalternate step includes identifying data-based encoded data slices ofthe plurality of encoded data blocks in accordance with thelong-term-storage-orientated dispersed storage error coding function. Asecond alternate step includes determining a data mapping of thedata-based encoded data slices between the set of DST unit and the atleast some of the DST units. A third alternate step includesfacilitating copying of at least some of the data-based encoded dataslices to the at least some of the DST units in accordance with the datamapping.

The method continues at step 884 where the processing module facilitatesstorage of the groupings of encoded data blocks in at least some of theset of DST units, where a DST unit of the at least some of the DST unitsrecovers a portion of the data from the one of the groupings of encodeddata blocks and performs a portion of the task on the recovered portionof the data to produce a partial task resultant. The method continues atstep 886 where the processing module receives partial task resultantsfrom the at least some of the set of DST units. The method continues atstep 888 where the processing module compiles the partial taskresultants to produce a task resultant.

FIG. 59 is a flowchart illustrating another example of transformingstored data. The method begins at step 890 where a processing module(e.g., of a distributed storage and task (DST) client module) determinesto convert data stored to facilitate a dispersed storage task to datastored to facilitate a distributed computing task. The determining mayinclude at least one of receiving a conversion request, determining toconvert based on one or more of a distributed computing task requestindicator, a data retrieval frequency indicator, a priority levelindicator, and a security level indicator. For example, the processingmodule detects that a series of steps of a task have been queued up toprocess the data utilizing a distributed computing approach anddetermines to convert dispersed storage format data to distributedcomputing format data to facilitate a distributed computing taskexecution efficiency improvement.

The method continues at step 892 where the processing module identifiesDST execution units utilized to store the data (e.g., a lookup). Themethod continues at step 894 where the processing module obtainsdispersed storage task processing parameters of the data. The obtainingincludes one or more of performing a lookup, receiving the parameters,accessing DST allocation information to extract the parameters,accessing a dispersed storage vault, and performing a query.

The method continues at step 896 where the processing module determinesdistributed computing task processing parameters of the data. Thedetermining includes one or more of performing a lookup, receiving theparameters, accessing DST allocation information to extract theparameters, performing a query, and determining the parameters based onone or more of a distributed computing requirement, and a performancerequirement.

The method continues at step 898 where the processing module obtains adispersed storage task pillar mapping corresponding to the data. Thedetermining includes one or more of performing a lookup, receiving themapping, accessing DST allocation information to extract the mapping,performing a query, and determining the mapping based on one or more ofa dispersed storage requirement, and a storage performance requirement.

The method continues at step 900 where the processing module determinesa distributed computing task pillar mapping corresponding to the data.The determining may be based on one or more of a network topology, adistributed computing performance requirement, a network bandwidthutilization maximum and one more storage objectives including minimizingtransfer of slices, and efficiently utilizing storage capacity. Forexample, the processing module determines to utilize DST execution unitslikely to be matched to steps of likely distributed computing tasks. Asanother example, the processing module determines to not transfer slicesfrom at least some of the DST execution units to minimize networkbandwidth utilization. As yet another example, the processing moduledetermines to leave same pillar number slices in different DST executionunits when the DST execution units are at the same site as indicated bythe network topology.

The method continues at step 902 where the processing module identifiesslice groupings to transfer based on the distributed computing taskpillar mapping and the dispersed storage task pillar mapping. The methodcontinues at step 904 where the processing module facilitates transferof slice groupings to transfer between two or more of the DST executionunits. The method continues at step 906 where the processing moduleupdates a directory and/or DST allocation information to indicate whereeach slice grouping is stored. Subsequent utilization of the data maysupport execution of tasks by each DST execution unit thus providing asystem level distributed computing performance improvement.Alternatively, the processing module may retrieve a decode thresholdnumber of slice groupings, decode the decode threshold number of slicegroupings to reproduce the data, generate new slice groupings inaccordance with the pillar mappings, and facilitate storing of the newslice groupings in accordance with the pillar mappings.

FIG. 60A is a diagram illustrating an example of non-sequential datasegment storage mapping that includes a directory 910, an anchor object912, and one or more data regions 1-2. The directory 910 provides anindex function for locating data objects stored within at least one of adistributed storage network (DSN) and a distributed storage and tasknetwork (DSTN). The directory 910 includes a data identifier (ID) field914, a distributed storage network (DSN) address field 916, and ananchor object format flag 918. The anchor object 912 includes datastorage mapping information. The data storage mapping informationincludes a link format indicator 920 and a segment allocation table(SAT) 922. Each data region of the one or more data regions 1-2 includesone or more data segments. For example, a first data region includesdata segments 1_1, 1_2, etc., through data segment 1_N when the firstdata region includes N data segments and a second data region includesdata segments 2_1, 2_2, etc., through data segment 2_M when the dataregion includes M data segments.

Each of the directory 910, the anchor object 912, and the one or moredata regions 1-2 is stored in one or more of a local memory and a DSNmemory. When utilizing the DSN memory, the directory 910 is encodedusing a dispersed storage error coding function to produce a set ofdirectory slices for storage in the DSN memory. When utilizing the DSNmemory, the anchor object 912 is encoded using the dispersed storageerror coding function to produce a set of anchor object slices forstorage in the DSN memory. When utilizing the DSN memory, each datasegment of one or more data segments of a region is encoded using thedispersed storage or coding function to produce a set of encoded dataslices for storage in the DSN memory. When utilizing the DSN memory, avault source name is assigned as a DSN address for an object to bestored in the DSN memory. For example, a first DSN address is assignedto the directory 910 and is utilized to generate a set of slice namescorresponding to the set of directory slices. As another example, asecond DSN address is assigned to the anchor object 912 and is utilizedto generate a set of slice names corresponding to the set of anchorobject slices. Such a second DSN address is utilized as an entry for theDSN address field 916.

DSN addresses are sequentially assigned to the one or more data segmentsof each data region. For example, a third DSN address is assigned to afirst data segment of the first data region, a fourth DSN address isassigned to a second data segment of the first data region, where thefourth DSN address includes a data segment identifier entry that issubstantially the same as a data segment identifier entry of the thirdDSN address incremented by one, a fifth DSN address is assigned to athird data segment of the first data region, where the fifth DSN addressincludes a data segment identifier entry that is substantially the sameas the data segment identifier entry of the fourth DSN addressincremented by one, etc. DSN addresses of each data segment of the oneor more data segments of each data region may be generated based on aDSN address of a first data segment of the data region and informationregarding the one or more data segments (e.g., how many data segments).Slice names of a set of slices associated with each data segment may begenerated based on the DSN address of the data segment (e.g., appendinga pillar index based on a pillar width of dispersed storage error codingparameters).

The SAT 922 includes one or more entries. Each entry includes identityof a corresponding data region (e.g., a region number), a DSN address ofthe first data segment, and information relating the first data segmentto the one or more data segments (e.g., a total size of the data region,a data segment size, a data segmentation approach, a number of datasegments). For example, a first entry of the SAT 922 includes a DSNaddress of data segment 1_1 and information that N data segments areincluded in region 1 and a second entry of SAT 922 includes a DSNaddress of data segment 2_1 and information that M data segments areincluded in region 2. As such, the SAT 922 provides access to the one ormore data segments of the one or more data regions when stored in theDSN memory.

In an example of operation, a data ID 924, that corresponds to datastored as one or more data segments of the one or more data regions, isutilized to identify an entry of directory 910 that includes a data IDentry in the data ID field 914 that substantially matches data ID 924.The DSN address field 916 is accessed to retrieve the DSN address entrycorresponding to the storage location of anchor object 912 and theanchor object format flag field 918 is accessed to retrieve an anchorobject format flag entry. The anchor object format flag entry identifieswhether the anchor object 912 includes a format indicator that includesthe linked format indicator 920.

Next, the anchor object 912 is retrieved using the DSN address 916 ofthe anchor object 912. When the anchor object format flag 918 indicatesthat the anchor object 912 includes the format indicator, the formatindicator is extracted from the anchor object 912. The format indicatorincludes at least one of a linked format indicator 920 and a packedformat indicator 928 as discussed with reference to FIG. 60B. When theformat indicator indicates the linked format indicator 920, the SAT 922is extracted and interpreted to identify the DSN address of the firstdata segment of each data region of the one or more data regions. Next,at least one data segment of the one or more data segments per dataregion is accessed utilizing one or more entries of the SAT 922.

FIG. 60B is a diagram illustrating an example of sequential data segmentstorage mapping that includes a directory 910, an anchor object 926, andone or more data regions 1-2. The directory 910 provides an indexfunction for locating data objects stored within at least one of adistributed storage network (DSN) and a distributed storage and tasknetwork (DSTN). The directory 910 includes a data identifier (ID) field914, a distributed storage network (DSN) address field 916, and ananchor object format flag 918. The anchor object 926 includes datastorage mapping information and a first data segment of a first dataregion of the one or more data regions 1-2. The data storage mappinginformation includes a packed format indicator 928 and a segmentallocation table (SAT) 922. The first data region of the one or moredata regions 1-2 includes the first data segment of the first dataregion and one or more remaining data segments of the one or more datasegments. Remaining data regions of the one or more data regions 1-2includes one or more data segments corresponding to the data region. Forexample, a first data region includes anchor object 926 (e.g., thatincludes data segment 1_1), data segments 1_2, 1_3, etc., through datasegment 1_N when the first data region includes N data segments and asecond data region includes data segments 2_1, 2_2, etc., through datasegment 2_M when the data region includes M data segments.

Each of the directory 910, the anchor object 926, and data segments ofthe one or more data regions 1-2 is stored in one or more of a localmemory and a DSN memory. When utilizing the DSN memory, the directory910 is encoded using a dispersed storage error coding function toproduce a set of directory slices for storage in the DSN memory. Whenutilizing the DSN memory, the anchor object 926 is encoded using thedispersed storage error coding function to produce a set of data storageinformation and data slices for storage in the DSN memory. Whenutilizing the DSN memory, each remaining data segment of one or moredata segments of the first region is encoded using the dispersed storageor coding function to produce a set of encoded data slices for storagein the DSN memory. When utilizing the DSN memory, a vault source name isassigned as a DSN address for an object to be stored in the DSN memory.For example, a first DSN address is assigned to the directory 910 and isutilized to generate a set of slice names corresponding to the set ofdirectory slices. As another example, a second DSN address is assignedto the anchor object 926 and is utilized to generate a set of slicenames corresponding to the set of data storage information and dataslices. Such a second DSN address is utilized as an entry for the DSNaddress field 916.

DSN addresses are sequentially assigned to the one or more data segmentsof each data region. For example, a third DSN address is assigned theanchor object 926 that includes the first data segment of the first dataregion, a fourth DSN address is assigned to a second data segment of thefirst data region, where the fourth DSN address includes a data segmentidentifier entry that is substantially the same as a data segmentidentifier entry of the third DSN address incremented by one, a fifthDSN address is assigned to a third data segment of the first dataregion, where the fifth DSN address includes a data segment identifierentry that is substantially the same as the data segment identifierentry of the fourth DSN address incremented by one, etc. DSN addressesof each data segment of the one or more data segments of each dataregion may be generated based on a DSN address of a first data segment(e.g., of the anchor object 926 for the first data region) of the dataregion and information regarding the one or more data segments (e.g.,how many data segments). Slice names of a set of slices associated witheach data segment may be generated based on the DSN address of the datasegment (e.g., appending a pillar index based on a pillar width ofdispersed storage error coding parameters).

The SAT 922 includes one or more entries. Each entry includes identityof a corresponding data region (e.g., a region number), a DSN address ofthe first data segment (e.g., of the anchor object 926 for the firstdata region), and information relating the first data segment to the oneor more data segments (e.g., a total size of the data region, a datasegment size, a data segmentation approach, a number of data segments).For example, a first entry of the SAT 922 includes a DSN address of theanchor object 926 that includes data segment 1_1 and information that Ndata segments are included in region 1 and a second entry of SAT 922includes a DSN address of data segment 2_1 and information that M datasegments are included in region 2. As such, the SAT 922 provides accessto the one or more data segments of the one or more data regions whenstored in the DSN memory.

In an example of operation, a data ID 924, that corresponds to datastored as one or more data segments of the one or more data regions, isutilized to identify an entry of directory 910 that includes a data IDentry in the data ID field 914 that substantially matches data ID 924.The DSN address field 916 is accessed to retrieve the DSN address entrycorresponding to the storage location of anchor object 926 and theanchor object format flag field 918 is accessed to retrieve an anchorobject format flag entry. The anchor object format flag entry identifieswhether the anchor object 912 includes a format indicator that includesthe packed format indicator 928.

Next, the anchor object 926 is retrieved using the DSN address 916 ofthe anchor object 926. When the anchor object format flag 918 indicatesthat the anchor object 926 includes the format indicator, the formatindicator is extracted from the anchor object 926. The format indicatorincludes at least one of a linked format indicator 920 and a packedformat indicator 928. When the format indicator indicates the packedformat indicator 928, the SAT 922 is extracted and interpreted toidentify the DSN address of the second data segment of the first dataregion. The first data segment is extracted from the anchor object 926.The SAT 922 is interpreted to identify the DSN address of the first datasegment of remaining data regions (e.g., starting with the second dataregion when the second data region exists). Next, at least one datasegment of the one or more data segments per data region is accessedutilizing one or more entries of the SAT 922. The storage mapping isdiscussed in greater detail with reference to FIGS. 60C-F.

FIG. 60C is a schematic block diagram of an embodiment of a distributedstorage network (DSN) that includes a computing device 930 and adistributed storage network (DSN) memory 932. The DSN memory 932includes a plurality of dispersed storage (DS) units 934. Each DS unit934 may be implemented by one or more of a distributed storage and task(DST) execution unit, a DST unit, a server, a user device, a memorydevice, and a DST processing unit. The computing device 930 may beimplemented by one or more of a DS unit 934, a DST unit, a DST executionunit, a DST client module, a distributed task (DT) execution module, aprocessing module, a controller, a user device, a DST processing unit, adistributed storage and task network (DSTN) managing unit, and a DSTintegrity processing unit. The computing device 930 includes adistributed storage (DS) module 936 (e.g., a distributed storageprocessing module). The DS module 936 includes a select storage module938, a segment module 940, a generate mapping module 942, an encodemodule 944, and an output module 946.

The system is operable to store data 948 in the DSN memory 932 (e.g.,also referred to as the DSN 932). The select storage module 938determines whether to use sequential data segment storage mapping ornon-sequential data segment storage mapping for storage of the data 948based on data read/write probabilities 950 of the data 948. The dataread/write probabilities 950 includes one or more of size of the data,data type, estimated regularity of editing at least a portion of thedata, estimated regularity of deleting at least a portion of the data,estimated regularity of expanding the data, storage capabilities of theDSN 932 (e.g., vault capacity, performance level, formats, etc.),parallel read/write preferences, and non-sequential read/writepreferences.

The segment module 940, when the non-sequential data segment storagemapping is to be used, performs a series of steps. A first step includesdetermining an initial set of storage regions of the DSN 932 for storingthe data 948. The segment module 940 determines the initial set ofstorage regions by determining the initial set of storage regions basedon one or more of the data read/write probabilities 950. A second stepincludes mapping a set of data partitions to the initial set of storageregions, where the data 948 is divided into the set of data partitions(e.g., data partitions may be the same size, different sizes, or acombination thereof). A third step includes, for each data partition ofthe set of data partitions, segmenting the data partition into aplurality of data segments 952. A fourth step includes, for each datapartition of the set of data partitions, designating a first datasegment of the plurality of data segments 952.

The generate mapping module 942, when the non-sequential data segmentstorage mapping is to be used, generates data storage mappinginformation 954 regarding at least one of the mapping of the set of datapartitions to the initial set of storage regions, the plurality of datasegments 952 for each data partition of the set of data partitions, andthe first data segment for each data partition of the set of datapartitions. The generate mapping module 942 generates the data storagemapping information 954 by a series of steps. A first step includesgenerating an indication for the non-sequential data segment storagemapping. A second step includes generating a segment allocation table,where an entry of the segment allocation table includes identity of oneof the initial set of storage regions, identity of the first datasegment, and information relating the first data segment to theplurality of data segments 952.

The encode module 944, when the non-sequential data segment storagemapping is to be used, performs a series of encoding steps. A firstencoding step includes encoding, in accordance with a first dispersedstorage error coding function, the data storage mapping information 954to produce at least one set of encoded mapping information slices 956. Asecond encoding step includes, for each data partition of the set ofdata partitions, encoding, in accordance with a second dispersed storageerror coding function, the plurality of data segments 952 to produce aplurality of sets of encoded data slices 958. The output module 946,when the non-sequential data segment storage mapping is to be used,outputs the at least one set of encoded mapping information slices 956and, for each data partition of the set of data partitions, theplurality of sets of encoded data slices 958 to the DSN 932 for storagetherein.

Alternatively, the DS module 936 (e.g., the encode module 944) combinesthe data storage mapping information 954 and the first data segment of afirst data partition of the set of data partitions for storage as acommon set of encoded mapping information and data slices 960. Whencombining, the first dispersed storage error coding function specifiesdispersed storage error coding parameters and the second dispersedstorage error coding function specifies the dispersed storage errorcoding parameters. When combining, the encode module 944 performs aseries of combining encoding steps. A first combining encoding stepincludes encoding, in accordance with the dispersed storage error codingparameters, the data storage mapping information 954 and the first datasegment of a first data partition of the set of data partitions toproduce at least one set of encoded mapping information and data slices960. A second combining encoding step includes, for each remaining datasegment of the plurality of data segments 952 of the first datapartition, encoding, in accordance with the dispersed storage errorcoding parameters, the remaining data segment to produce a set ofencoded data slices 958 (e.g., for output by the output module 946 tothe DSN 932).

Alternatively, the select storage module 938 may determine to utilizethe sequential data segment storage mapping. When the sequential datasegment storage mapping is to be used, the segment module 940 segmentsthe data 948 into a plurality of data segments 952 and designates afirst data segment of the plurality of data segments 952. The generatemapping module 942 generates data storage mapping information 954regarding at least one of the plurality of data segments 952 and thefirst data segment. The encode module 944 encodes, in accordance with adispersed storage error coding function, the data storage mappinginformation 954 and the first data segment to produce at least one setof encoded mapping information and data slices 960. For each remainingdata segment of the plurality of data segments 952, the encode module944 encodes, in accordance with the dispersed storage error codingfunction, the remaining data segment to produce a set of encoded dataslices 958. The output module 946 outputs at least one set of encodedmapping information and data slices 960 and, for each remaining datasegment of the plurality of data segments, the set of encoded dataslices 958 to the DSN 932 for storage therein.

The DS module 936 may receive additional data 962 to store with the data948. When receiving additional data 962, the select storage module 938receives the additional data 962 to store with the data 948. The segmentmodule 940, when receiving additional data 962, performs a series ofadditional steps. A first additional step includes selecting anotherstorage region of the DSN 932 for storing the additional data 962. Asecond additional step includes updating a set of storage regions toinclude the initial set of storage regions and the other storage region.A third additional step includes segmenting the additional data 962 intoan additional plurality of data segments 952. A fourth additional stepincludes designating a first data segment of the additional plurality ofdata segments 952.

When receiving the additional data 962, the generate mapping module 942updates the data storage mapping information 954 to include at least oneof the mapping of the set of data partitions and the additional data tothe set of storage regions, the additional plurality of data segments952, and the first data segment of the additional plurality of datasegments 952. The encode module 944 performs additional steps includinga first step where the encode module 944 encodes, in accordance with thefirst dispersed storage error coding function, the updated data storagemapping information to produce at least one updated set of encodedmapping information slices 956. In a second additional step, the encodemodule 944 encodes, in accordance with the second dispersed storageerror coding function, the additional plurality of data segments toproduce an additional plurality of sets of encoded data slices 958. Theoutput module 944 outputs the at least one updated set of encodedmapping information slices 956 and the additional plurality of sets ofencoded data slices 958 to the DSN 932 for storage therein.

FIG. 60D is a flowchart illustrating another example of storing data.The method begins at step 970 where a processing module (e.g., of adispersed storage processing module) determines whether to usesequential data segment storage mapping or non-sequential data segmentstorage mapping for storage of data based on data read/writeprobabilities of the data. The method branches to step 984 when theprocessing module determines to use non-sequential data segment storagemapping. The method continues to step 972 when the processing moduledetermines to use sequential data segment storage mapping. The methodcontinues at step 972 where the processing module segments the data intoa plurality of data segments when the sequential data segment storagemapping is to be used. The method continues at step 974 where theprocessing module designates a first data segment of the plurality ofdata segments.

The method continues at step 976 where the processing module generatesdata storage mapping information regarding at least one of the pluralityof data segments and the first data segment. The generating data storagemapping information includes generating an indication for the sequentialdata segment storage mapping and generating a segment allocation table.An entry of the segment allocation table includes identity of the firstdata segment (e.g., a vault source name) and information relating thefirst data segment to the plurality of data segments (e.g., a number ofdata segments, a total length of the plurality of data segments).

The method continues at step 978 where the processing module encodes, inaccordance with a dispersed storage error coding function, the datastorage mapping information and the first data segment to produce atleast one set of encoded mapping information and data slices. For eachremaining data segment of the plurality of data segments, the methodcontinues at step 980 where the processing module encodes, in accordancewith the dispersed storage error coding function, the remaining datasegment to produce a set of encoded data slices. The method continues atstep 982 where the processing module outputs at least one set of encodedmapping information and data slices and, for each remaining data segmentof the plurality of data segments, the set of encoded data slices to adistributed storage network (DSN) for storage therein.

When the non-sequential data segment storage mapping is to be used, themethod continues at step 984 where the processing module determines aninitial set of storage regions of the DSN for storing the data. Thedetermining the initial set of storage regions includes determining theinitial set of storage regions based on one or more of the dataread/write probabilities. The method continues at step 986 where theprocessing module maps a set of data partitions to the initial set ofstorage regions, where the data is divided into the set of datapartitions (e.g., data partitions can be the same size, different sizes,or a combination thereof). For each data partition of the set of datapartitions, the method continues at step 988 where the processing modulesegments the data partition into a plurality of data segments anddesignates a first data segment of the plurality of data segments (e.g.,identifies a vault source name).

The method continues at step 992 where the processing module generatesdata storage mapping information regarding at least one of the mappingof the set of data partitions to the initial set of storage regions, theplurality of data segments for each data partition of the set of datapartitions, and the first data segment for each data partition of theset of data partitions. The generating data storage mapping informationincludes generating an indication for the non-sequential data segmentstorage mapping and generating the segment allocation table. An entry ofthe segment allocation table includes identity of one of the initial setof storage regions (e.g., a region identifier), identity of the firstdata segment (e.g., vault source name), and information relating thefirst data segment to the plurality of data segments (e.g., a number ofdata segments, a total length of the plurality of data segments).

The processing module may utilize a packed format for the first datasegment, where the first data segment is combined with the data storagemapping information when utilizing the packed format. The processingmodule may determine to utilize the packed format based on one or moreof the data read/write probabilities and the indication for thenon-sequential data segment storage mapping. For example, the processingmodule selects the packed format when the data read/write probabilitiesindicate that a data size of the data is less than a packed datathreshold size. As another example, the processing module selects thenon-packed format when the non-sequential data segment format isindicated. The method branches to step 1000 when the processing moduleselects the packed format. The method continues to step 994 when theprocessing module selects a non-packed format.

The method continues at step 994 where the processing module encodes, inaccordance with a first dispersed storage error coding function, thedata storage mapping information to produce at least one set of encodedmapping information slices. For each data partition of the set of datapartitions, the method continues at step 996 where the processing moduleencodes, in accordance with a second dispersed storage error codingfunction, the plurality of data segments to produce a plurality of setsof encoded data slices. The method continues at step 998 where theprocessing module outputs the at least one set of encoded mappinginformation slices and, for each data partition of the set of datapartitions, the plurality of sets of encoded data slices to the DSN forstorage therein. When receiving additional data, the method branches tostep 1006.

When using the packed format, the first dispersed storage error codingfunction specifies dispersed storage error coding parameters, the seconddispersed storage error coding function specifies the dispersed storageerror coding parameters, and the method continues at step 1000 where theprocessing module encodes, in accordance with the dispersed storageerror coding parameters, the data storage mapping information and thefirst data segment of a first data partition of the set of datapartitions to produce at least one set of encoded mapping informationand data slices. For each remaining data segment of the plurality ofdata segments of the first data partition, the method continues at step1002 where the processing module encodes, in accordance with thedispersed storage error coding parameters, the remaining data segment toproduce a set of encoded data slices. The method continues at step 1004where the processing module outputs the at least one set of encodedmapping information and data slices and, for each remaining data segmentof the plurality of data segments of the first data partition, the setof encoded data slices to the DSN for storage therein. When receivingthe additional data, the method continues to step 1006.

The method continues at step 1006 where the processing module receivesthe additional data to store with the data. The method continues at step1008 where the processing module selects another storage region of theDSN for storing the additional data and updates a set of storage regionsto include the initial set of storage regions and the other storageregion. The method continues at step 1012 where the processing modulesegments the additional data into an additional plurality of datasegments and designates a first data segment of the additional pluralityof data segments. The method continues at step 1016 where the processingmodule updates the data storage mapping information to include at leastone of the mapping of the set of data partitions and the additional datato the set of storage regions, the additional plurality of datasegments, and the first data segment of the additional plurality of datasegments.

The method continues at step 1018 where the processing module encodes,in accordance with the first dispersed storage error coding function,the updated data storage mapping information to produce at least oneupdated set of encoded mapping information slices. The method continuesat step 1020 where the processing module encodes, in accordance with thesecond dispersed storage error coding function, the additional pluralityof data segments to produce an additional plurality of sets of encodeddata slices. The method continues at step 1022 where the processingmodule outputs the at least one updated set of encoded mappinginformation slices and the additional plurality of sets of encoded dataslices to the DSN for storage therein.

FIG. 60E is a schematic block diagram of another embodiment of adistributed storage network (DSN) that includes a computing device 1030and a distributed storage network (DSN) memory 932. The DSN memory 932includes a plurality of dispersed storage (DS) units 934. Each DS unit934 may be implemented by one or more of a distributed storage and task(DST) execution unit, a DST unit, a server, a user device, a memorydevice, and a DST processing unit. The computing device 1030 may beimplemented by one or more of a DS unit 934, a DST unit, a DST executionunit, a DST client module, a distributed task (DT) execution module, aprocessing module, a controller, a user device, a DST processing unit, adistributed storage and task network (DSTN) managing unit, and a DSTintegrity processing unit. The computing device 1030 includes adistributed storage (DS) module 1032 (e.g., a distributed storageprocessing module). The DS module 1032 includes a segment module 1034, agenerate mapping module 1036, an encode module 1038, and an outputmodule 1040.

The system is operable to store data 948 in the DSN memory 932 (e.g.,also referred to as the DSN 932). The segment module 1034 performs aseries of steps. A first step includes mapping a set of data partitionsto a set of storage regions, where the data 948 is divided into the setof data partitions (e.g., data partitions can be the same size,different sizes, or a combination thereof). The segment module 1034 mapsthe set of data partitions to the set of storage regions by determiningthe set of storage regions based on data read/write probabilities. Thedata read/write probabilities includes one or more of size of the data,data type, estimated regularity of editing at least a portion of thedata, estimated regularity of deleting at least a portion of the data,estimated regularity of expanding the data, storage capabilities of theDSN, parallel read/write preferences, and non-sequential read/writepreferences. For each data partition of the set of data partitions, thesegment module 1034 segments the data partition into a plurality of datasegments 952 and designates a first data segment of the plurality ofdata segments (e.g., identifies a vault source name).

The generate mapping module 1036 generates data storage mappinginformation 954 regarding at least one of the mapping of the set of datapartitions to the set of storage regions, the plurality of data segments952 for each data partition of the set of data partitions, and the firstdata segment for each data partition of the set of data partitions. Thegenerate mapping module 1036 generates the data storage mappinginformation 954 by a series of steps. A first step includes generatingan indication for the non-sequential data segment storage mapping. Asecond step includes generating a segment allocation table. An entry ofthe segment allocation table includes identity of one of the initial setof storage regions, identity of the first data segment, and informationrelating the first data segment to the plurality of data segments 952.

The encode module 1038 performs a series of steps. A first step includesencoding, in accordance with a first dispersed storage error codingfunction, the data storage mapping information 954 to produce at leastone set of encoded mapping information slices 956. For each datapartition of the set of data partitions, a second step includesencoding, in accordance with a second dispersed storage error codingfunction, the plurality of data segments 952 to produce a plurality ofsets of encoded data slices 958. The output module 1040 outputs the atleast one set of encoded mapping information slices 956 and, for eachdata partition of the set of data partitions, the plurality of sets ofencoded data slices 958 to the DSN 932 for storage therein.

Alternatively, the DS module 1032 (e.g., the encode module 1038)combines the data storage mapping information 954 and the first datasegment of a first data partition of the set of data partitions forstorage as a common set of encoded mapping information and data slices960. When combining, the first dispersed storage error coding functionspecifies dispersed storage error coding parameters and the seconddispersed storage error coding function specifies the dispersed storageerror coding parameters. When combining, the encode module 1038 performsa series of combining encoding steps. A first combining encoding stepincludes encoding, in accordance with the dispersed storage error codingparameters, the data storage mapping information 954 and the first datasegment of a first data partition of the set of data partitions toproduce at least one set of encoded mapping information and data slices960 (e.g., for output by the output module 1040 to the DSN 932). Asecond combining encoding step includes, for each remaining data segmentof the plurality of data segments 952 of the first data partition,encoding, in accordance with the dispersed storage error codingparameters, the remaining data segment to produce a set of encoded dataslices 958 (e.g., for output by the output module 1040 to the DSN 932).

The DS module 1032 may receive additional data 962 to store with thedata 948. When receiving the additional data 962, the segment module1034 performs a series of steps. A first step includes receiving theadditional data 962 to store with the data 932. A second step includesselecting another storage region of the DSN 932 for storing theadditional data 962. A third step includes updating the set of storageregions to include the other storage region. A fourth step includessegmenting the additional data 962 into an additional plurality of datasegments 952. A fifth step includes designating a first data segment ofthe additional plurality of data segments 952. When receiving theadditional data 962, the generate mapping module 1036 updates the datastorage mapping information 954 to include at least one of the mappingof the set of data partitions and the additional data 962 to the updatedset of storage regions, the additional plurality of data segments 952,and the first data segment of the additional plurality of data segments952.

When receiving the additional data 962, the encode module 1038 performsa series of steps. A first step includes encoding, in accordance withthe first dispersed storage error coding function, the updated datastorage mapping information 954 to produce at least one updated set ofencoded mapping information slices 956. A second step includes encoding,in accordance with the second dispersed storage error coding function,the additional plurality of data segments 952 to produce an additionalplurality of sets of encoded data slices 958. When receiving theadditional data, the output module 1040 outputs the at least one updatedset of encoded mapping information slices 956 and the additionalplurality of sets of encoded data slices 958 to the DSN 932 for storagetherein.

FIG. 60F is a flowchart illustrating another example of storing data,which includes similar steps to FIG. 60D. The method begins at step 1042where a processing module (e.g., of a dispersed storage processingmodule) maps a set of data partitions to a set of storage regions, wherethe data is divided into the set of data partitions. The mapping the setof data partitions to the set of storage regions includes determiningthe set of storage regions based on data read/write probabilities. Foreach data partition of the set of data partitions, the method continueswith step 988 of FIG. 60D where the processing module segments the datapartition into a plurality of data segments and designates a first datasegment of the plurality of data segments (e.g., identifies a vaultsource name).

The method continues at step 1044 where the processing module generatesdata storage mapping information regarding at least one of the mappingof the set of data partitions to the set of storage regions, theplurality of data segments for each data partition of the set of datapartitions, and the first data segment for each data partition of theset of data partitions. The generating data storage mapping informationincludes a series of steps. A first step includes generating anindication for the non-sequential data segment storage mapping. A secondstep includes generating a segment allocation table. An entry of thesegment allocation table includes identity of one of the initial set ofstorage regions, identity of the first data segment, and informationrelating the first data segment to the plurality of data segments.

The processing module may utilize a packed format for the first datasegment, where the first data segment is combined with the data storagemapping information when utilizing the packed format. The processingmodule may determine to utilize the packed format based on one or moreof the data read/write probabilities. For example, the processing moduleselects the packed format when the data read/write probabilitiesindicate that a data size of the data is less than a packed datathreshold size. The method branches to step 1000 of FIG. 60D when theprocessing module selects the packed format. The method continues tostep 994 of FIG. 60D when the processing module selects a non-packedformat.

The method continues with step 994 of FIG. 60D where the processingmodule encodes, in accordance with a first dispersed storage errorcoding function, the data storage mapping information to produce atleast one set of encoded mapping information slices. For each datapartition of the set of data partitions, the method continues at step996 where the processing module encodes, in accordance with a seconddispersed storage error coding function, the plurality of data segmentsto produce a plurality of sets of encoded data slices. The methodcontinues with step 998 of FIG. 60D where the processing module outputsthe at least one set of encoded mapping information slices and, for eachdata partition of the set of data partitions, the plurality of sets ofencoded data slices to the DSN for storage therein. When receivingadditional data, the method branches to step 1006 of FIG. 60D.

When using the packed format, the first dispersed storage error codingfunction specifies dispersed storage error coding parameters, the seconddispersed storage error coding function specifies the dispersed storageerror coding parameters, and the method continues with step 1000 of FIG.60D where the processing module encodes, in accordance with thedispersed storage error coding parameters, the data storage mappinginformation and the first data segment of a first data partition of theset of data partitions to produce at least one set of encoded mappinginformation and data slices. For each remaining data segment of theplurality of data segments of the first data partition, the methodcontinues with step 1002 of FIG. 60D where the processing moduleencodes, in accordance with the dispersed storage error codingparameters, the remaining data segment to produce a set of encoded dataslices. The method continues with step 1004 of FIG. 60D where theprocessing module outputs the at least one set of encoded mappinginformation and data slices and, for each remaining data segment of theplurality of data segments of the first data partition, the set ofencoded data slices to the DSN for storage therein. When receiving theadditional data, the method continues to step 1006 of FIG. 60D.

The method continues with step 1006 of FIG. 60D where the processingmodule receives the additional data to store with the data. The methodcontinues at step 1046 where the processing module selects anotherstorage region of the DSN for storing the additional data. The methodcontinues at step 1048 where the processing module updates the set ofstorage regions to include the other storage region. The methodcontinues with step 1012 of FIG. 60D where the processing modulesegments the additional data into an additional plurality of datasegments and designates a first data segment of the additional pluralityof data segments. The method continues with step 1016 of FIG. 60D wherethe processing module updates the data storage mapping information toinclude at least one of the mapping of the set of data partitions andthe additional data to the set of storage regions, the additionalplurality of data segments, and the first data segment of the additionalplurality of data segments.

The method continues with step 1018 of FIG. 60D where the processingmodule encodes, in accordance with the first dispersed storage errorcoding function, the updated data storage mapping information to produceat least one updated set of encoded mapping information slices. Themethod continues with step 1020 of FIG. 60D where the processing moduleencodes, in accordance with the second dispersed storage error codingfunction, the additional plurality of data segments to produce anadditional plurality of sets of encoded data slices. The methodcontinues with step 1022 of FIG. 60D where the processing module outputsthe at least one updated set of encoded mapping information slices andthe additional plurality of sets of encoded data slices to the DSN forstorage therein.

FIG. 61A is a schematic block diagram of another embodiment of adistributed storage network (DSN) that includes a computing device 1060and a DSN memory 932. The DSN memory 932 includes a plurality ofdispersed storage (DS) units 934. Each DS unit 934 may be implemented byone or more of a distributed storage and task (DST) execution unit, aDST unit, a server, a user device, a memory device, and a DST processingunit. The computing device 1060 may be implemented by one or more of aDS unit 934, a DST unit, a DST execution unit, a DST client module, adistributed task (DT) execution module, a processing module, acontroller, a user device, a DST processing unit, a distributed storageand task network (DSTN) managing unit, and a DST integrity processingunit. The computing device 1060 includes a distributed storage (DS)module 1062 (e.g., a distributed storage processing module). The DSmodule 1062 includes a receive module 1064, an index module 1066, anidentify module 1068, and a retrieve module 1070.

The system is operable to retrieve a portion 1072 of a data object(e.g., data file, video file, text file, multimedia file, data file,etc.) from the DSN memory 932 (e.g., also referred to as the DSN 932).The receive module 1064 receives a request 1074 to retrieve the portion1072 of the data object that is stored in the DSN 932, where the requestincludes a DSN address for data storage mapping information 1076regarding storage of the data object. The request may also include oneor more data interpretation parameters to facilitate identification ofthe portion 1072 of the data object. The data interpretation parametersincludes one or more of interpretation-based separators for datafiltering, interpretation-based separators for data access, chapters ofthe data object, pages of the data object, sub-chapters of the dataobject, markers within the data object, time codes associated with thedata object, and run-time divisions for playback of the data object.

The data storage mapping information 1076 includes at least one of aplurality of subgroups of data storage mapping information. A firstsubgroup of data storage mapping information includes a mapping of a setof data partitions to storage regions, where the data object ispartitioned into the set of data partitions. A second subgroup of datastorage mapping information includes information regarding data segmentsas a plurality of data segments for each data partition of the set ofdata partitions (e.g., a number of data segments, a total size of thedata partition, a data segmentation scheme). A third subgroup of datastorage mapping information includes information regarding a first datasegment of the plurality of data segments for each data partition of theset of data partitions. (e.g., a vault source name DSN address).

The index module 1066 performs a series of indexing steps. A firstindexing step includes retrieving, based on the DSN address, the datastorage mapping information 1076, which maps storage of the data objectas data segments in data storage regions of the DSN in accordance withdata storage optimization parameters. The data storage optimizationparameters includes one or more of storage efficiency, storagereliability, DSN performance, data security, and read/writeprobabilities of the data object.

The index module 1066 retrieves the data storage mapping information1076 by a series of retrieving steps. A first retrieving step includesretrieving at least a decode threshold number of at least one set ofencoded mapping information slices 956 from a set of DS units 934 of theDSN 932 based on the DSN address. For example, the index module 1066performs a directory lookup to identify the DSN address based on a dataidentifier of the data object. Next, the index module 1066 generates atleast one set of slice names corresponding to the at least one set ofencoded mapping information slices based on the identified DSN address.The index module 1066 generates at least one set of read slice requeststhat includes the at least one set of slice names. Next, the indexmodule 1066 outputs the at least one set of read slice requests to theDSN memory 932. The index module 1066 receives the at least a decodethreshold number of the at least one set of encoded mapping informationslices 956. A second retrieving step includes the index module 1066decoding, in accordance with a dispersed storage error coding function,the at least a decode threshold number of the at least one set ofencoded mapping information slices 956 to produce the data storagemapping information 1076.

A second indexing step includes accessing, by the index module 1066,based on the request, indexing information 1078 regarding the dataobject, where the indexing information 1078 identifies a categorizationof the data object into a plurality of categorical data portions 1080 inaccordance with data interpretation parameters. The categorizationincludes user-based interpretation separators for data filtering, datafinding, etc. (e.g., chapters, pages, some-chapters, markers, timecodes, runtime divisions for playback of a file, etc.). For example, afirst categorical data portion includes identifying a first data segmentof each region of the set of regions. As another example, a secondcategorical data portion includes five minutes of video from a point 45minutes into a video file. The accessing includes at least one ofretrieving, generating, receiving, and obtaining. For example, the indexmodule 1066 accesses the indexing information 1078 regarding the data byperforming a distributed computing function on the data object stored inthe DSN to generate the indexing information 1078. In such an example,the index module 1066 retrieves at least some encoded data slices 958 ofthe data object from the DSN memory 932 to facilitate performing thedistributed computing function on the data object.

The identify module 1068 performs a series of identification steps. Afirst identification step includes identifying, for the portion of thedata object, a specific categorical data portion of the plurality ofcategorical data portions 1080 in accordance with the indexinginformation 1078. A second identification step includes equating thespecific categorical data portion to specific storage information 1082of the data storage mapping information 1076 to identify at least onedata segment of the data segments of at least one storage region of thestorage regions. The identify module 1068 equates the specificcategorical data portion to the specific storage information 1082 by aseries of equating steps. A first equating step includes aligning theindexing information 1078 with the data storage mapping information 1076to establish a common reference point. A second equating step includesidentifying a target reference point with respect to the commonreference point for the specific categorical data portion. A thirdequating step includes utilizing the target reference point to identifythe specific storage information 1082 of the data storage mappinginformation 1076 with respect to the common reference point.

The retrieve module 1070 retrieves the at least one data segment of theat least one storage region from the DSN in accordance with the specificstorage information 1082. The retrieve module 1070 retrieves the atleast one data segment by, for each data segment of the at least onedata segment of the at least one storage region, retrieving at least adecode threshold number of encoded data slices 958 from the DSN 932 anddecoding, in accordance with a dispersed storage error coding function,the at least a decode threshold number of encoded data slices 958 toreproduce the data segment. The retrieve module 1070 outputs the atleast one data segment as the portion 1072 of the data object.

FIG. 61B is a flowchart illustrating an example of retrieving data. Themethod begins at step 1090 where a processing module (e.g., of adispersed storage (DS) processing module) receives a request to retrievea portion of a data object (e.g., data file, video file, etc.) that isstored in a distributed storage network (DSN), where the requestincludes a DSN address for data storage mapping information regardingstorage of the data object. The request may also include one or more ofthe data interpretation parameters to facilitate identification of theportion of the data object.

The method continues at step 1092 where the processing module retrieves,based on the DSN address, the data storage mapping information, whichmaps storage of the data object as data segments in data storage regionsof the DSN in accordance with data storage optimization parameters. Theretrieving the data storage mapping information includes a series ofretrieving steps. A first retrieving step includes the processing moduleretrieving at least a decode threshold number of at least one set ofencoded mapping information slices from a set of dispersed storage unitsof the DSN based on the DSN address. A second retrieving step includesthe processing module decoding, in accordance with a dispersed storageerror coding function, the at least a decode threshold number of the atleast one set of encoded mapping information slices to produce the datastorage mapping information.

The method continues at step 1094 where the processing module accesses,based on the request, indexing information regarding the data object,where the indexing information identifies a categorization of the dataobject into a plurality of categorical data portions in accordance withdata interpretation parameters. The accessing the indexing informationregarding the data includes performing a distributed computing functionon the data object stored in the DSN to generate the indexinginformation. The method continues at step 1096 where the processingmodule identifies, for the portion of the data object, a specificcategorical data portion of the plurality of categorical data portionsin accordance with the indexing information.

The method continues at step 1098 where the processing module equatesthe specific categorical data portion to specific storage information ofthe data storage mapping information to identify at least one datasegment of the data segments of at least one storage region of thestorage regions. The equating the specific categorical data portion tospecific storage information includes a series of equating steps. Afirst equating step includes the processing module aligning the indexinginformation with the data storage mapping information to establish acommon reference point. A second equating step includes the processingmodule identifying a target reference point with respect to the commonreference point for the specific categorical data portion. A thirdequating step includes the processing module utilizing the targetreference point to identify the specific storage information of the datastorage mapping information with respect to the common reference point.

The method continues at step 1100 where the processing module retrievesthe at least one data segment of the at least one storage region fromthe DSN in accordance with the specific storage information. Theretrieving the at least one data segment includes, for each data segmentof the at least one data segment of the at least one storage region, theprocessing module retrieving at least a decode threshold number ofencoded data slices from the DSN and the processing module decoding, inaccordance with a dispersed storage error coding function, the at leasta decode threshold number of encoded data slices to reproduce the datasegment.

FIG. 62 is a flowchart illustrating an example of upgrading software.The method begins with step 1102 where a processing module (e.g., of adistributed storage and task network (DSTN) managing unit) determines toperform a software upgrade on a set of distributed storage and task(DST) execution units. The determining may be based on one or more ofreceiving a software upgrade from a software source, receiving asoftware upgrade request, a software revision usage timeout, an errormessage, a software defect detection indicator, and a manager input.

The method continues at step 1104 where the processing module obtainsdispersal parameters utilized by the set of DST execution units. Theobtaining includes at least one of receiving the parameters, initiatinga query, performing a lookup, and accessing a vault associated with theset of DST execution units. The method continues at step 1106 where theprocessing module obtains availability information of the set of DSTexecution units. The availability information includes at least one of acurrently active indicator, a currently inactive indicator, a scheduleddowntime indicator, a downtime history record, a software revisionindicator, and a current error indicator. The obtaining includes atleast one of receiving the information, initiating a query, performing alookup, and performing an availability test with one or more DSTexecution units of the set of DST execution units.

The method continues at step 1108 where the processing module selectsone or more DST execution units of the set of DST execution units asselected units to upgrade. The selecting may be based on one or more ofdispersed storage error coding parameters, a maximum number ofunavailable DST execution units, a minimum number of available DSTexecution units, availability information, a software revisionindicator, a timestamp associated with a last software upgrade, anupgrade schedule, and a last software upgrade success indicator. Forexample, the processing module selects at most a pillar width minus adecode threshold number of units such that there are at least a decodethreshold number of DST execution units online and available (e.g., notcurrently being upgraded and available to access data). As anotherexample, the processing module selects a DST execution unit that isassociated with a previously unsuccessful software upgrade whenremaining DST execution units of the set of DST execution units have allbeen upgraded.

The method continues at step 1110 where the processing modulefacilitates identifying the selected units as unavailable. Thefacilitating includes at least one of sending a message to the selectedunits and updating a DST execution unit availability table. The methodcontinues at step 1112 where the processing module initiates a softwareupgrade for the selected units. The initiating includes at least one ofsending software and an upgrade request to each DST execution unit ofthe selected units, and sending an upgrade request and an address of thesoftware for retrieval to each DST execution unit of the selected units.

The method continues at step 1114 where the processing module receives asoftware upgrade response from a DST execution unit of the selectedunits. The software upgrade response includes at least one of asuccessful upgrade indicator and an unsuccessful upgrade indicator. Themethod continues at step 1116 where the processing module facilitatesidentifying the DST execution unit of the selected units as availablewhen the software upgrade response is favorable. The facilitatingincludes at least one of sending an available message to the DSTexecution unit and updating the DST execution unit availability table toindicate that the DST execution unit is available and upgraded.

The method continues at 1118 where the processing module determineswhether the set of DST execution units are all upgraded (e.g., bycomparing a number of upgraded DST execution units from the DSTexecution unit availability table to the number of DST execution units).The method repeats back to step 1106 when the processing moduledetermines that the set of DST execution units are not all upgraded. Themethod continues to step 1120 when the processing module determines thatthe set of DST execution units are all upgraded. The method continues atstep 1120 where the method ends. In addition, the processing module mayoutput an indicator that the upgrade process has completed.

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “operably coupled to”, “coupled to”, and/or “coupling” includesdirect coupling between items and/or indirect coupling between items viaan intervening item (e.g., an item includes, but is not limited to, acomponent, an element, a circuit, and/or a module) where, for indirectcoupling, the intervening item does not modify the information of asignal but may adjust its current level, voltage level, and/or powerlevel. As may further be used herein, inferred coupling (i.e., where oneelement is coupled to another element by inference) includes direct andindirect coupling between two items in the same manner as “coupled to”.As may even further be used herein, the term “operable to” or “operablycoupled to” indicates that an item includes one or more of powerconnections, input(s), output(s), etc., to perform, when activated, oneor more its corresponding functions and may further include inferredcoupling to one or more other items. As may still further be usedherein, the term “associated with”, includes direct and/or indirectcoupling of separate items and/or one item being embedded within anotheritem. As may be used herein, the term “compares favorably”, indicatesthat a comparison between two or more items, signals, etc., provides adesired relationship. For example, when the desired relationship is thatsignal 1 has a greater magnitude than signal 2, a favorable comparisonmay be achieved when the magnitude of signal 1 is greater than that ofsignal 2 or when the magnitude of signal 2 is less than that of signal1.

As may also be used herein, the terms “processing module”, “processingcircuit”, and/or “processing unit” may be a single processing device ora plurality of processing devices. Such a processing device may be amicroprocessor, micro-controller, digital signal processor,microcomputer, central processing unit, field programmable gate array,programmable logic device, state machine, logic circuitry, analogcircuitry, digital circuitry, and/or any device that manipulates signals(analog and/or digital) based on hard coding of the circuitry and/oroperational instructions. The processing module, module, processingcircuit, and/or processing unit may be, or further include, memoryand/or an integrated memory element, which may be a single memorydevice, a plurality of memory devices, and/or embedded circuitry ofanother processing module, module, processing circuit, and/or processingunit. Such a memory device may be a read-only memory, random accessmemory, volatile memory, non-volatile memory, static memory, dynamicmemory, flash memory, cache memory, and/or any device that storesdigital information. Note that if the processing module, module,processing circuit, and/or processing unit includes more than oneprocessing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

The present invention has been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claimed invention. Further, theboundaries of these functional building blocks have been arbitrarilydefined for convenience of description. Alternate boundaries could bedefined as long as the certain significant functions are appropriatelyperformed. Similarly, flow diagram blocks may also have been arbitrarilydefined herein to illustrate certain significant functionality. To theextent used, the flow diagram block boundaries and sequence could havebeen defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claimed invention. One of average skill in the artwill also recognize that the functional building blocks, and otherillustrative blocks, modules and components herein, can be implementedas illustrated or by discrete components, application specificintegrated circuits, processors executing appropriate software and thelike or any combination thereof.

The present invention may have also been described, at least in part, interms of one or more embodiments. An embodiment of the present inventionis used herein to illustrate the present invention, an aspect thereof, afeature thereof, a concept thereof, and/or an example thereof. Aphysical embodiment of an apparatus, an article of manufacture, amachine, and/or of a process that embodies the present invention mayinclude one or more of the aspects, features, concepts, examples, etc.,described with reference to one or more of the embodiments discussedherein. Further, from figure to figure, the embodiments may incorporatethe same or similarly named functions, steps, modules, etc., that mayuse the same or different reference numbers and, as such, the functions,steps, modules, etc., may be the same or similar functions, steps,modules, etc., or different ones.

While the transistors in the above described figure(s) is/are shown asfield effect transistors (FETs), as one of ordinary skill in the artwill appreciate, the transistors may be implemented using any type oftransistor structure including, but not limited to, bipolar, metal oxidesemiconductor field effect transistors (MOSFET), N-well transistors,P-well transistors, enhancement mode, depletion mode, and zero voltagethreshold (VT) transistors.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of the various embodimentsof the present invention. A module includes a processing module, afunctional block, hardware, and/or software stored on memory forperforming one or more functions as may be described herein. Note that,if the module is implemented via hardware, the hardware may operateindependently and/or in conjunction software and/or firmware. As usedherein, a module may contain one or more sub-modules, each of which maybe one or more modules.

While particular combinations of various functions and features of thepresent invention have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent invention is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method for execution by a computing device, themethod comprises: dividing data into data partitions; for a datapartition of the data partitions: associating indexing information withthe data partition; segmenting the data partition into a plurality ofdata segments; dispersed storage error encoding the plurality of datasegments to produce a plurality of sets of encoded data slices; andgrouping encoded data slices of the plurality of sets of encoded dataslices to produce a set of groupings of encoded data slices.
 2. Themethod of claim 1, wherein the segmenting the data partition comprises:dividing the data partition into a plurality of data blocks; arrangingthe data blocks into a predetermined number of rows and columns toproduce an ordered arrangement of data blocks; and creating theplurality of data segments by segmenting the ordered arrangement of datablocks and associating the indexing information therewith.
 3. The methodof claim 2, wherein the grouping the encoded data slices comprises: foreach set of encoded data slices of the plurality of sets of encoded dataslices: placing a first encoded data slice in a first grouping ofencoded data slices; placing a second encoded data slice in a secondgrouping of encoded data slices; and placing a third encoded data slicesin a third grouping of encoded data slices, such that the first groupingof encoded data slices includes encoding data slices corresponding to afirst portion of the ordered arrangement of data blocks, the secondgrouping of encoded data slices includes encoding data slicescorresponding to a second portion of the ordered arrangement of datablocks, and the third grouping of encoded data slices includes encodingdata slices corresponding to a third portion of the ordered arrangementof data blocks.
 4. The method of claim 1 further comprises: sending theset of groupings of encoded data slices to a set of storage units of adispersed storage network for storage therein.
 5. The method of claim 1,wherein the indexing information comprises one or more of: indexgeneration task information; a data index; and data indexing taskinformation.
 6. The method of claim 5, wherein the index generation taskinformation comprises one or more of: a search parameter, a keyword,pattern recognition information, and timing information.
 7. The methodof claim 5, wherein the data index comprises: metadata associated withthe data, wherein the metadata includes one or more of keywords, dates,internet protocol addresses, partial content, word counts, statistics, asummary, a distributed storage and task network (DSTN) address, a DSTNaddress corresponding to data index storage, and a DSTN addresscorresponding to index data storage.
 8. The method of claim 5, whereinthe data indexing task information comprises one or more of: datareduction instructions, a keyword filter, a data index reference, and anindexed data format.
 9. The method of claim 1, wherein the associatingthe indexing information with the data partition comprises: ascertainingsearchable features of the data; determining whether the searchablefeatures are affiliated with existing indexing information; when thesearchable features are affiliated with existing indexing information,associating the existing indexing information with the data partition;and when the searchable features are not affiliated with existingindexing information, creating the indexing information.
 10. Anon-transitory computer readable memory device comprises: a first memorysection that stores operational instructions that, when executed by acomputing device, causes the computing device to: divide data into datapartitions; a second memory section that stores operational instructionsthat, when executed by the computing device, causes the computing deviceto: for a data partition of the data partitions: associate indexinginformation with the data partition; segment the data partition into aplurality of data segments; dispersed storage error encode the pluralityof data segments to produce a plurality of sets of encoded data slices;and group encoded data slices of the plurality of sets of encoded dataslices to produce a set of groupings of encoded data slices.
 11. Thenon-transitory computer readable memory device of claim 10, wherein thesecond memory section further stores operational instructions that, whenexecuted by the computing device, causes the computing device to segmentthe data partition by: dividing the data partition into a plurality ofdata blocks; arranging the data blocks into a predetermined number ofrows and columns to produce an ordered arrangement of data blocks; andcreating the plurality of data segments by segmenting the orderedarrangement of data blocks and associating the indexing informationtherewith.
 12. The non-transitory computer readable memory device ofclaim 11, wherein the second memory section further stores operationalinstructions that, when executed by the computing device, causes thecomputing device to group the encoded data slices by: for each set ofencoded data slices of the plurality of sets of encoded data slices:placing a first encoded data slice in a first grouping of encoded dataslices; placing a second encoded data slice in a second grouping ofencoded data slices; and placing a third encoded data slices in a thirdgrouping of encoded data slices, such that the first grouping of encodeddata slices includes encoding data slices corresponding to a firstportion of the ordered arrangement of data blocks, the second groupingof encoded data slices includes encoding data slices corresponding to asecond portion of the ordered arrangement of data blocks, and the thirdgrouping of encoded data slices includes encoding data slicescorresponding to a third portion of the ordered arrangement of datablocks.
 13. The non-transitory computer readable memory device of claim10, wherein the second memory section further stores operationalinstructions that, when executed by the computing device, causes thecomputing device to: send the set of groupings of encoded data slices toa set of storage units of a dispersed storage network for storagetherein.
 14. The non-transitory computer readable memory device of claim10, wherein the indexing information comprises one or more of: indexgeneration task information; a data index; and data indexing taskinformation.
 15. The non-transitory computer readable memory device ofclaim 14, wherein the index generation task information comprises one ormore of: a search parameter, a keyword, pattern recognition information,and timing information.
 16. The non-transitory computer readable memorydevice of claim 14, wherein the data index comprises: metadataassociated with the data, wherein the metadata includes one or more ofkeywords, dates, internet protocol addresses, partial content, wordcounts, statistics, a summary, a distributed storage and task network(DSTN) address, a DSTN address corresponding to data index storage, anda DSTN address corresponding to index data storage.
 17. Thenon-transitory computer readable memory device of claim 10, wherein thedata indexing task information comprises one or more of: data reductioninstructions, a keyword filter, a data index reference, and an indexeddata format.
 18. The non-transitory computer readable memory device ofclaim 10, wherein the second memory section further stores operationalinstructions that, when executed by the computing device, causes thecomputing device to associate the indexing information with the datapartition by: ascertaining searchable features of the data; determiningwhether the searchable features are affiliated with existing indexinginformation; when the searchable features are affiliated with existingindexing information, associating the existing indexing information withthe data partition; and when the searchable features are not affiliatedwith existing indexing information, creating the indexing information.